From cf68a8aa03fea9a751e1469aa664bf09ffcd74f5 Mon Sep 17 00:00:00 2001
From: palatej <palatej@users.noreply.github.com>
Date: Tue, 12 Dec 2023 08:02:53 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20rjdemetr?=
 =?UTF-8?q?a/rjd3tramoseats@3670409eeae9019899236a6680a5500423fddf4f=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                        | 2 +-
 authors.html                                    | 6 +++---
 index.html                                      | 2 +-
 pkgdown.yml                                     | 2 +-
 reference/deprecated-rjd3tramoseats.html        | 2 +-
 reference/index.html                            | 2 +-
 reference/jd3_utilities.html                    | 2 +-
 reference/refresh.html                          | 2 +-
 reference/seats_decompose.html                  | 2 +-
 reference/set_seats.html                        | 2 +-
 reference/terror.html                           | 2 +-
 reference/tramo.html                            | 2 +-
 reference/tramo_forecast.html                   | 2 +-
 reference/tramo_outliers.html                   | 2 +-
 reference/tramoseats.html                       | 2 +-
 reference/tramoseats_dictionary.html            | 2 +-
 reference/tramoseats_revisions.html             | 2 +-
 reference/tramoseats_spec.html                  | 2 +-
 reference/userdefined_variables_tramoseats.html | 2 +-
 search.json                                     | 2 +-
 20 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/404.html b/404.html
index c23e39c..0c0995a 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://rjdemetra.github.io/rjd3tramoseats/index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index 3c95527..e4582d5 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -59,13 +59,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Palate J, Quartier-la-Tente A (2023).
 <em>rjd3tramoseats: Seasonal Adjustment with TRAMO-SEATS and 'JDemetra+ 3.0'</em>.
-R package version 3.2.1, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/, <a href="https://github.com/rjdemetra/rjd3tramoseats" class="external-link">https://github.com/rjdemetra/rjd3tramoseats</a>. 
+R package version 3.2.2, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/, <a href="https://github.com/rjdemetra/rjd3tramoseats" class="external-link">https://github.com/rjdemetra/rjd3tramoseats</a>. 
 </p>
       <pre>@Manual{,
   title = {rjd3tramoseats: Seasonal Adjustment with TRAMO-SEATS and 'JDemetra+ 3.0'},
   author = {Jean Palate and Alain Quartier-la-Tente},
   year = {2023},
-  note = {R package version 3.2.1, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/},
+  note = {R package version 3.2.2, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/},
   url = {https://github.com/rjdemetra/rjd3tramoseats},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index b8bf13a..0b6c291 100644
--- a/index.html
+++ b/index.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/pkgdown.yml b/pkgdown.yml
index 8672186..e95fe28 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2023-12-12T08:00Z
+last_built: 2023-12-12T08:02Z
 urls:
   reference: https://rjdemetra.github.io/rjd3tramoseats/reference
   article: https://rjdemetra.github.io/rjd3tramoseats/articles
diff --git a/reference/deprecated-rjd3tramoseats.html b/reference/deprecated-rjd3tramoseats.html
index 0c5fa89..fdb706c 100644
--- a/reference/deprecated-rjd3tramoseats.html
+++ b/reference/deprecated-rjd3tramoseats.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index c9958fd..afc9345 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/jd3_utilities.html b/reference/jd3_utilities.html
index 63d2155..2d9c102 100644
--- a/reference/jd3_utilities.html
+++ b/reference/jd3_utilities.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/refresh.html b/reference/refresh.html
index 80a8c5c..4f7f757 100644
--- a/reference/refresh.html
+++ b/reference/refresh.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/seats_decompose.html b/reference/seats_decompose.html
index 2feeb1b..e4390d2 100644
--- a/reference/seats_decompose.html
+++ b/reference/seats_decompose.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/set_seats.html b/reference/set_seats.html
index cb7f34c..7432f0b 100644
--- a/reference/set_seats.html
+++ b/reference/set_seats.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/terror.html b/reference/terror.html
index ee7a629..60a0e5c 100644
--- a/reference/terror.html
+++ b/reference/terror.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramo.html b/reference/tramo.html
index dcf14e6..acc664b 100644
--- a/reference/tramo.html
+++ b/reference/tramo.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramo_forecast.html b/reference/tramo_forecast.html
index 81ab419..0a70092 100644
--- a/reference/tramo_forecast.html
+++ b/reference/tramo_forecast.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramo_outliers.html b/reference/tramo_outliers.html
index 24526e2..49334f4 100644
--- a/reference/tramo_outliers.html
+++ b/reference/tramo_outliers.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramoseats.html b/reference/tramoseats.html
index d2c9c24..03a71c2 100644
--- a/reference/tramoseats.html
+++ b/reference/tramoseats.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramoseats_dictionary.html b/reference/tramoseats_dictionary.html
index f7e2fb3..a4f5e19 100644
--- a/reference/tramoseats_dictionary.html
+++ b/reference/tramoseats_dictionary.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramoseats_revisions.html b/reference/tramoseats_revisions.html
index c33aae3..75dfe27 100644
--- a/reference/tramoseats_revisions.html
+++ b/reference/tramoseats_revisions.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/tramoseats_spec.html b/reference/tramoseats_spec.html
index 6a54bd8..a48a37d 100644
--- a/reference/tramoseats_spec.html
+++ b/reference/tramoseats_spec.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/userdefined_variables_tramoseats.html b/reference/userdefined_variables_tramoseats.html
index 9fb8426..c38f7e5 100644
--- a/reference/userdefined_variables_tramoseats.html
+++ b/reference/userdefined_variables_tramoseats.html
@@ -18,7 +18,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">rjd3tramoseats</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.2.2</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index af2f8bd..bf8fdef 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://rjdemetra.github.io/rjd3tramoseats/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jean Palate. Author, maintainer. Alain Quartier-la-Tente. Author.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palate J, Quartier-la-Tente (2023). rjd3tramoseats: Seasonal Adjustment TRAMO-SEATS 'JDemetra+ 3.0'. R package version 3.2.1, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/, https://github.com/rjdemetra/rjd3tramoseats.","code":"@Manual{,   title = {rjd3tramoseats: Seasonal Adjustment with TRAMO-SEATS and 'JDemetra+ 3.0'},   author = {Jean Palate and Alain Quartier-la-Tente},   year = {2023},   note = {R package version 3.2.1, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/},   url = {https://github.com/rjdemetra/rjd3tramoseats}, }"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"rjd3tramoseats","dir":"","previous_headings":"","what":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"rjd3tramoseats offers full acces options outputs TRAMO-SEATS (rjd3tramoseats::tramoseats()), including TRAMO modelling (rjd3tramoseats::tramo()) SEATS decomposition (rjd3tramoseats::seats_decompose()). specification can created functions rjd3tramoseats::tramo_spec() rjd3tramoseats::tramoseats_spec() can modified function: pre-processing: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(); decomposition: rjd3tramoseats::set_seats(); benchmarking: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"get current stable version (latest release): get current development version GitHub:","code":"# install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3toolkit@*release\") remotes::install_github(\"rjdemetra/rjd3tramoseats@*release\") # Install development version from GitHub # install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3tramoseats\")"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"get final components can use function rjd3toolkit::sa_decomposition():","code":"library(\"rjd3tramoseats\") y <- rjd3toolkit::ABS$X0.2.09.10.M ts_model <- tramoseats(y)  summary(ts_model$result$preprocessing) # Summary of tramo model #> Log-transformation: yes  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error  T-stat Pr(>|t|)     #> theta(1)  -0.82783    0.02571 -32.196  < 2e-16 *** #> btheta(1) -0.42554    0.06388  -6.661 9.01e-11 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Regression model: #>                   Estimate Std. Error T-stat Pr(>|t|)     #> monday          -0.0109446  0.0034805 -3.145 0.001788 **  #> tuesday          0.0048940  0.0035307  1.386 0.166479     #> wednesday        0.0001761  0.0034970  0.050 0.959867     #> thursday         0.0132928  0.0035330  3.763 0.000193 *** #> friday          -0.0024801  0.0035383 -0.701 0.483747     #> saturday         0.0153509  0.0035171  4.365 1.62e-05 *** #> lp               0.0410667  0.0101178  4.059 5.93e-05 *** #> easter           0.0503888  0.0072698  6.931 1.68e-11 *** #> AO (2000-06-01)  0.1681662  0.0299743  5.610 3.78e-08 *** #> AO (2000-07-01) -0.1972348  0.0298664 -6.604 1.28e-10 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> Number of observations:  425 , Number of effective observations:  412 , Number of parameters:  13  #> Loglikelihood:  781.358, Adjusted loglikelihood:  -2086.269 #> Standard error of the regression (ML estimate):  0.03615788  #> AIC:  4198.538 , AICc:  4199.452 , BIC:  4250.811 plot(ts_model) # Plot of the final decomposition rjd3toolkit::sa_decomposition(ts_model) #> Last values #>          series       sa    trend      seas       irr #> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979 #> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681 #> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508 #> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588 #> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587 #> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303 #> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913 #> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187 #> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924 #> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726 #> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781 #> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated-rjd3tramoseats","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"Deprecated functions","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"","code":"fast_tramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  fast_tramo(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )  spec_tramoseats(   name = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\") )  spec_tramo(name = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"))"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"ts, spec, context, userdefined, name Parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":null,"dir":"Reference","previous_headings":"","what":"Java Utility Functions — jd3_utilities","title":"Java Utility Functions — jd3_utilities","text":"functions used JDemetra+ 3.0 packages easily interact R Java objects.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Java Utility Functions — jd3_utilities","text":"","code":".tramoseats_rslts(jrslts)  .jd2r_spec_tramo(jspec)  .r2jd_spec_tramo(spec)  .jd2r_spec_tramoseats(jspec)  .r2jd_spec_tramoseats(spec)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Java Utility Functions — jd3_utilities","text":"spec, jspec, jrslts parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Refresh a specification with constraints — refresh","title":"Refresh a specification with constraints — refresh","text":"Function allowing create new specification updating specification used previous estimation. selected parameters kept fixed (previous estimation results) others freed re-estimation domain constraints. See details examples.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refresh a specification with constraints — refresh","text":"","code":"tramo_refresh(   spec,   refspec = NULL,   policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\",     \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\"),   period = 0,   start = NULL,   end = NULL )  tramoseats_refresh(   spec,   refspec = NULL,   policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\",     \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"),   period = 0,   start = NULL,   end = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refresh a specification with constraints — refresh","text":"spec current specification refreshed (\"result_spec\"). refspec reference specification used define domain considered re-estimation (\"domain_spec\"). default \"TRfull\" \"RSAfull\" specification. policy refresh policy apply (see details). period, start, end specify span outliers re-identified (.e.: re-detected) policy = \"Outliers\" policy = \"Outliers_StochasticComponent\". Span definition: period: numeric, number observations year (12, 4...). start end: first last date outliers re-identfied, defined arrays two elements: year first period (example, period = 12, c(1980, 1) January 1980). specified, outliers re-identified whole series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refresh a specification with constraints — refresh","text":"new specification, object class \"JD3_TRAMOSEATS_SPEC\" \"JD3_TRAMO_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refresh a specification with constraints — refresh","text":"selection constraints kept fixed re-estimated called revision policy. User-defined parameters always copied new refreshed specifications. Tramo (reg-arima pre-adjustment part) refreshed, final ARIMA model handed SEATS decomposition part might changed result refreshing process. Available refresh policies : Current: applying current pre-adjustment reg-arima model adding new raw data points Additive Outliers (defined new intervention variables) Fixed: applying current pre-adjustment reg-arima model replacing forecasts new raw data points. FixedParameters: pre-adjustment reg-arima model partially modified: regression coefficients re-estimated regression variables, Arima orders coefficients unchanged. FixedAutoRegressiveParameters: FixedParameters Arima Moving Average coefficients (MA) also re-estimated, Auto-regressive (AR) coefficients kept fixed. using Seats decomposition avoids possible re-allocation roots trend seasonal components, might led strong revisions. FreeParameters: regression Arima model coefficients re-estimated, regression variables Arima orders kept fixed. Outliers: regression variables Arima orders kept fixed, outliers re-detected defined span, thus regression Arima model coefficients re-estimated Outliers_StochasticComponent: \"Outliers\" Arima model orders (p,d,q)(P,D,Q) can also re-identified.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refresh a specification with constraints — refresh","text":"information revision policies JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/t-rev-policies-production","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Refresh a specification with constraints — refresh","text":"","code":"y<- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <-window(y,end = 2009) # raw series for second (refreshed) estimation y_new <-window(y,end = 2010) # specification for first estimation spec_ts_1<-tramoseats_spec(\"RSAFull\") # first estimation sa_ts<- tramoseats(y_raw, spec_ts_1) # refreshing the specification current_result_spec <- sa_ts$result_spec current_domain_spec <- sa_ts$estimation_spec spec_ts_ref <- tramoseats_refresh(current_result_spec, # point spec to be refreshed   current_domain_spec, #domain spec (set of constraints)   policy = \"FixedAutoRegressiveParameters\") # 2nd estimation with refreshed specification sa_ts_ref <- tramoseats(y_new, spec_ts_ref)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":null,"dir":"Reference","previous_headings":"","what":"SEATS Decomposition — seats_decompose","title":"SEATS Decomposition — seats_decompose","text":"SEATS Decomposition","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SEATS Decomposition — seats_decompose","text":"","code":"seats_decompose(   sarima,   seas.tolerance = 2,   trend.boundary = 0.5,   seas.boundary = 0.8,   seas.boundary.unique = 0.8,   approximation = c(\"None\", \"Legacy\", \"Noisy\") )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SEATS Decomposition — seats_decompose","text":"sarima SARIMA model (see rjd3toolkit::sarima_model()). seas.tolerance numeric: seasonal tolerance (epsphi). tolerance (measured degrees) allocate AR non-real roots seasonal component (modulus inverse complex AR root greater trend boundary frequency root differs one seasonal frequencies less Seasonal tolerance) transitory component (otherwise). Possible values [0,10]. Default value 2. trend.boundary numeric: trend boundary (rmod). boundary beyond AR root integrated trend component. modulus inverse real root greater trend boundary, AR root integrated trend component. value, root integrated transitory component. Possible values [0,1]. Default=0.5. seas.boundary numeric: seasonal boundary (sbound). boundary beyond real negative AR root integrated seasonal component. modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.boundary.unique numeric: seasonal boundary (unique),(sboundatpi). boundary beyond negative AR root integrated seasonal component, root unique seasonal root.  modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. approximation character: approximation mode. ARIMA model estimated TRAMO accept admissible decomposition, SEATS: \"None\" - performs approximation; \"Legacy\" - replaces model decomposable one; \"Noisy\" - estimates new model adding white noise non-admissible model estimated TRAMO. Default=\"Legacy\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SEATS Decomposition — seats_decompose","text":"","code":"seats_decompose(rjd3toolkit::sarima_model(period = 12,phi = c(0,1),bd = 1)) #> model  #>  #> AR:  1 0 1  #> DIF:  1 0 0 0 0 0 0 0 0 0 0 0 -1  #> var:  1  #>  #> cmp-1  #>  #> DIF:  1 -1  #> MA:  1 1  #> var:  0.0004340278  #>  #> cmp-2  #>  #> AR:  1 0 1  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.5913351 0.2222453 0.0112871 -0.1344438 -0.2247946 -0.2715816 -0.2845599 -0.2706347 -0.2410118 -0.1721786 -0.1381599 -0.04996781 -0.03753463  #> var:  0.5974421  #>  #> cmp-3  #>  #> var:  0.0228588  #>"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Seats Specification — set_seats","title":"Set Seats Specification — set_seats","text":"Function allowing customize parameters decomposition part (Seats) Tramo-Seats seasonal adjustment process. (Seats Arima Model Based decomposition algorithm working conjunction Tramo.)","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Seats Specification — set_seats","text":"","code":"set_seats(   x,   approximation = c(NA, \"None\", \"Legacy\", \"Noisy\"),   trend.boundary = NA,   seas.boundary = NA,   seas.boundary.unique = NA,   seas.tolerance = NA,   ma.boundary = NA,   fcasts = NA,   bcasts = NA,   algorithm = c(NA, \"Burman\", \"KalmanSmoother\"),   bias = NA )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Seats Specification — set_seats","text":"x specification modified, object class \"JD3_TRAMOSEATS_SPEC\", generated tramoseats_spec() function approximation character: approximation mode. ARIMA model estimated TRAMO accept admissible decomposition, SEATS: \"None\" - performs approximation; \"Legacy\" - replaces model decomposable one; \"Noisy\" - estimates new model adding white noise non-admissible model estimated TRAMO. Default=\"Legacy\". trend.boundary numeric: trend boundary (rmod). boundary beyond AR root integrated trend component. modulus inverse real root greater trend boundary, AR root integrated trend component. value, root integrated transitory component. Possible values [0,1]. Default=0.5. seas.boundary numeric: seasonal boundary (sbound). boundary beyond real negative AR root integrated seasonal component. modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.boundary.unique numeric: seasonal boundary (unique),(sboundatpi). boundary beyond negative AR root integrated seasonal component, root unique seasonal root.  modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.tolerance numeric: seasonal tolerance (epsphi). tolerance (measured degrees) allocate AR non-real roots seasonal component (modulus inverse complex AR root greater trend boundary frequency root differs one seasonal frequencies less Seasonal tolerance) transitory component (otherwise). Possible values [0,10]. Default value 2. ma.boundary numeric: MA unit root boundary. modulus estimated MA root falls range [xl, 1], set xl. Possible values [0.9,1]. Default=0.95. bcasts, fcasts numeric: number backasts (bcasts) forecasts (fcasts) used decomposition periods (positive values) years (negative values).Default bcasts=0. Default fcasts=0. algorithm character: estimation method unobserved components. choice can made : \"Burman\": default value. May result significant underestimation components' standard deviation, may become numerically unstable roots MA polynomial near 1; \"KalmanSmoother\": disturbed (quasi-) unit roots MA; bias TODO.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Seats Specification — set_seats","text":"object class  \"JD3_TRAMOSEATS_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Set Seats Specification — set_seats","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Seats Specification — set_seats","text":"","code":"init_spec<-tramoseats_spec(\"rsafull\") new_spec<- set_seats(init_spec,                     approximation = \"Legacy\",                     trend.boundary = 0.8,                     seas.boundary = 0.5,                     fcasts = -3,                     algorithm = \"KalmanSmoother\",                     bias = TRUE) y <- rjd3toolkit::ABS$X0.2.09.10.M sa<- tramoseats(y,spec=new_spec)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":null,"dir":"Reference","previous_headings":"","what":"TERROR Quality Control of Outliers — terror","title":"TERROR Quality Control of Outliers — terror","text":"TRAMO ERRORs (TERROR) controls quality data checking outliers end series","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TERROR Quality Control of Outliers — terror","text":"","code":"terror(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   nback = 1,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TERROR Quality Control of Outliers — terror","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. nback number last observations considered quality check. context dictionnary variables.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TERROR Quality Control of Outliers — terror","text":"mts object 7 variables: actual actual data end series. forecast forecast actual data end series. error absolute errors (= observed - forecasts). rel.error relative errors (\"scores\") : ratios forecast errors standard deviation forecasts last observations (positive values mean -estimation). raw transformed series. especially, chosen model implies log-transformation, values obtained log-transformation. transformations, leap year corrections length-periods corrections may also used. fraw forecast transformed series. efraw absolute errors transformed series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TERROR Quality Control of Outliers — terror","text":"","code":"terror(rjd3toolkit::ABS$X0.2.09.10.M, nback = 2) #>          actual forecast     error rel. error transformed tr.fcast   tr.error #> Jul 2017 1445.5 1468.903 -23.40336 -0.4328439    7.276211 7.292271 0.03710535 #> Aug 2017 1303.1 1341.213 -38.11282 -0.7664734    7.172501 7.201330 0.03761155"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"TRAMO model, pre-adjustment TRAMO-SEATS","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"","code":"tramo(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )  tramo_fast(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context dictionnary variables. userdefined vector containing additional output variables (see tramoseats_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"tramo() function returns list results (\"JD3_regarima_rslts\" object), estimation specification result specification, tramo_fast() faster function returns results.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"","code":"library(\"rjd3toolkit\") #>  #> Attaching package: ‘rjd3toolkit’ #> The following objects are masked from ‘package:stats’: #>  #>     aggregate, mad y = rjd3toolkit::ABS$X0.2.09.10.M sp = tramo_spec(\"trfull\") sp = add_outlier(sp,                  type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) tramo_fast(y, spec = sp) #> Log-transformation: yes  #> SARIMA model:  (2,1,2) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> phi(1)    -0.09251    0.39861 -0.232 #> phi(2)     0.12325    0.08546  1.442 #> theta(1)  -1.02295    0.40059 -2.554 #> theta(2)   0.24537    0.31899  0.769 #> btheta(1) -0.43575    0.06277 -6.942 #>  #> Regression model: #>                   Estimate Std. Error T-stat #> monday          -1.131e-02  3.604e-03 -3.137 #> tuesday          5.831e-03  3.623e-03  1.609 #> wednesday       -8.502e-05  3.605e-03 -0.024 #> thursday         1.293e-02  3.665e-03  3.529 #> friday          -2.076e-03  3.646e-03 -0.569 #> saturday         1.535e-02  3.635e-03  4.224 #> lp               3.881e-02  9.934e-03  3.907 #> easter           5.207e-02  7.566e-03  6.882 #> AO (2010-01-01)  3.674e-02  2.922e-02  1.258 #> AO (2015-01-01) -9.888e-03  2.935e-02 -0.337 #> AO (2000-06-01)  1.736e-01  2.928e-02  5.928 #> AO (2000-07-01) -1.831e-01  2.918e-02 -6.273 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  18  #>  #> Loglikelihood:  787.6949  #> Adjusted loglikelihood:  -2079.932  #>  #> Standard error of the regression (ML estimate):  0.03560084  #> AIC:  4195.864  #> AICC:  4197.604  #> BIC:  4268.242  #>  sp = set_transform(   set_tradingdays(     set_easter(sp, enabled = FALSE),     option = \"workingdays\"   ),   fun = \"None\" ) tramo_fast(y, spec = sp) #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>  sp = set_outlier(sp, outliers.type = c(\"AO\")) tramo_fast(y, spec = sp) #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecasts with TRAMO — tramo_forecast","title":"Forecasts with TRAMO — tramo_forecast","text":"Forecasts TRAMO","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecasts with TRAMO — tramo_forecast","text":"","code":"tramo_forecast(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   nf = -1,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecasts with TRAMO — tramo_forecast","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. nf forecasting horizon (numeric). forecast length periods (positive values) years (negative values). default, program generates one-year forecast (nf = -1). context dictionnary variables.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forecasts with TRAMO — tramo_forecast","text":"mts object 7 variables: forecast forecast actual data end series. error standard deviation forecast. fraw forecast transformed series. efraw standard deviation forecast transformed series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecasts with TRAMO — tramo_forecast","text":"","code":"tramo_forecast(rjd3toolkit::ABS$X0.2.09.10.M) #>          forecast     error     fraw      efraw #> Sep 2017 1394.524  51.63894 7.240309 0.03699180 #> Oct 2017 1465.852  55.09481 7.290192 0.03754581 #> Nov 2017 1719.851  65.39924 7.449993 0.03798498 #> Dec 2017 2766.983 107.04362 7.925513 0.03864273 #> Jan 2018 1460.928  57.22647 7.286827 0.03912637 #> Feb 2018 1090.905  43.08353 6.994763 0.03944732 #> Mar 2018 1447.182  58.38462 7.277374 0.04029456 #> Apr 2018 1395.393  57.07914 7.240932 0.04085424 #> May 2018 1482.589  60.88645 7.301545 0.04101588 #> Jun 2018 1556.902  64.92067 7.350453 0.04164443 #> Jul 2018 1433.030  60.29701 7.267546 0.04202090 #> Aug 2018 1302.717  55.47047 7.172207 0.04252290"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection with a Tramo Model — tramo_outliers","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"Tramo particular regarima model estimation algorithm, mainly used linearized series performing decomposition Seats","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"","code":"tramo_outliers(   y,   order = c(0L, 1L, 1L),   seasonal = c(0L, 1L, 1L),   mean = F,   X = NULL,   X.td = NULL,   ao = T,   ls = T,   tc = F,   so = F,   cv = 0,   ml = F,   clean = F )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"y dependent variable (ts object). order, seasonal orders ARIMA model. mean Boolean include mean. X user defined regressors (calendar). X.td calendar regressors. ao, ls, , tc Boolean indicate type outliers detected. cv numeric. entered critical value outliers' detection procedure. equal 0 critical value outliers' detection procedure automatically determined number observations. ml Use maximum likelihood (otherwise approximation means Hannan-Rissanen). clean Clean missing values beginning/end series. Regression variables automatically resized, need .","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"\"JD3_REGARIMA_OUTLIERS\" object.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"","code":"tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M) #> $model #> $model$y #>         Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct #> 1982                       460.1  502.6  443.8  459.1  438.4  465.1  452.7 #> 1983  379.2  378.0  472.1  503.4  510.6  462.4  468.3  458.2  482.7  485.3 #> 1984  414.7  414.5  484.7  487.3  597.9  500.4  543.4  503.4  522.8  556.6 #> 1985  516.3  452.5  525.8  587.7  700.3  561.8  602.8  582.5  563.1  637.1 #> 1986  570.5  478.2  547.4  594.3  751.6  553.4  663.2  581.1  661.9  665.6 #> 1987  613.9  513.2  599.9  674.1  714.0  670.5  720.9  601.6  672.3  709.1 #> 1988  631.0  551.1  678.1  715.7  740.8  722.0  683.5  650.9  723.3  729.6 #> 1989  631.5  552.0  719.0  697.6  764.8  786.3  715.1  723.8  757.9  751.7 #> 1990  678.2  586.2  726.8  744.1  815.5  832.4  710.3  759.4  741.1  786.6 #> 1991  694.0  604.7  719.2  748.2  828.2  746.9  794.5  770.4  741.5  858.6 #> 1992  740.0  665.9  701.5  831.4  878.6  826.0  788.2  723.6  819.8  902.5 #> 1993  762.1  643.0  754.1  840.7  906.6  887.1  771.5  728.7  844.7  886.9 #> 1994  745.7  664.4  821.5  831.7  908.0  912.6  782.9  798.8  887.0  934.6 #> 1995  752.4  682.5  811.2  906.0  927.2  906.8  880.6  873.9  856.8  920.6 #> 1996  833.1  737.1  812.0  895.2  962.8  908.6  908.0  888.9  833.7  933.7 #> 1997  840.9  727.4  857.9  849.0  994.8  830.2  971.1  836.0  939.1  976.9 #> 1998  917.3  716.2  822.9  970.1  970.2  849.4 1042.3  869.9  939.4 1021.3 #> 1999  942.0  738.4  903.2  953.2 1011.2  894.4 1054.5  899.5 1002.3 1043.7 #> 2000  924.9  798.2  901.9 1024.7 1052.3 1165.5  859.3 1009.2 1054.6 1070.4 #> 2001  971.9  814.6 1017.5 1039.2 1123.5 1024.9 1100.8  963.0 1012.9 1132.0 #> 2002 1027.9  841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4 #> 2003 1099.3  900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1 #> 2004 1182.9  989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3 #> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4 #> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6 #> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6 #> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4 #> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3 #> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9 #> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7 #> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6 #> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3 #> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7 #> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3 #> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4 #> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1               #>         Nov    Dec #> 1982  522.9  889.3 #> 1983  568.7  963.7 #> 1984  623.2 1039.4 #> 1985  697.1 1187.5 #> 1986  700.9 1367.9 #> 1987  743.2 1460.1 #> 1988  870.3 1570.0 #> 1989  923.8 1569.4 #> 1990  931.5 1563.1 #> 1991  944.7 1600.3 #> 1992  968.6 1650.9 #> 1993  970.0 1710.5 #> 1994 1000.4 1817.5 #> 1995 1067.4 1857.2 #> 1996 1081.6 1837.6 #> 1997 1111.3 1879.1 #> 1998 1137.7 1975.7 #> 1999 1207.2 2069.6 #> 2000 1232.5 2177.5 #> 2001 1344.8 2269.5 #> 2002 1495.1 2338.6 #> 2003 1528.2 2424.2 #> 2004 1536.7 2500.8 #> 2005 1540.4 2536.0 #> 2006 1623.3 2611.1 #> 2007 1687.9 2747.0 #> 2008 1675.9 2886.1 #> 2009 1736.6 2795.1 #> 2010 1705.5 2752.4 #> 2011 1660.0 2730.5 #> 2012 1672.9 2753.3 #> 2013 1668.9 2725.5 #> 2014 1687.7 2756.9 #> 2015 1730.5 2913.6 #> 2016 1684.3 2850.4 #> 2017               #>  #> $model$variables #> [1] \"AO.220\" \"AO.219\" #>  #> $model$X #>        [,1] [,2] #>   [1,]    0    0 #>   [2,]    0    0 #>   [3,]    0    0 #>   [4,]    0    0 #>   [5,]    0    0 #>   [6,]    0    0 #>   [7,]    0    0 #>   [8,]    0    0 #>   [9,]    0    0 #>  [10,]    0    0 #>  [11,]    0    0 #>  [12,]    0    0 #>  [13,]    0    0 #>  [14,]    0    0 #>  [15,]    0    0 #>  [16,]    0    0 #>  [17,]    0    0 #>  [18,]    0    0 #>  [19,]    0    0 #>  [20,]    0    0 #>  [21,]    0    0 #>  [22,]    0    0 #>  [23,]    0    0 #>  [24,]    0    0 #>  [25,]    0    0 #>  [26,]    0    0 #>  [27,]    0    0 #>  [28,]    0    0 #>  [29,]    0    0 #>  [30,]    0    0 #>  [31,]    0    0 #>  [32,]    0    0 #>  [33,]    0    0 #>  [34,]    0    0 #>  [35,]    0    0 #>  [36,]    0    0 #>  [37,]    0    0 #>  [38,]    0    0 #>  [39,]    0    0 #>  [40,]    0    0 #>  [41,]    0    0 #>  [42,]    0    0 #>  [43,]    0    0 #>  [44,]    0    0 #>  [45,]    0    0 #>  [46,]    0    0 #>  [47,]    0    0 #>  [48,]    0    0 #>  [49,]    0    0 #>  [50,]    0    0 #>  [51,]    0    0 #>  [52,]    0    0 #>  [53,]    0    0 #>  [54,]    0    0 #>  [55,]    0    0 #>  [56,]    0    0 #>  [57,]    0    0 #>  [58,]    0    0 #>  [59,]    0    0 #>  [60,]    0    0 #>  [61,]    0    0 #>  [62,]    0    0 #>  [63,]    0    0 #>  [64,]    0    0 #>  [65,]    0    0 #>  [66,]    0    0 #>  [67,]    0    0 #>  [68,]    0    0 #>  [69,]    0    0 #>  [70,]    0    0 #>  [71,]    0    0 #>  [72,]    0    0 #>  [73,]    0    0 #>  [74,]    0    0 #>  [75,]    0    0 #>  [76,]    0    0 #>  [77,]    0    0 #>  [78,]    0    0 #>  [79,]    0    0 #>  [80,]    0    0 #>  [81,]    0    0 #>  [82,]    0    0 #>  [83,]    0    0 #>  [84,]    0    0 #>  [85,]    0    0 #>  [86,]    0    0 #>  [87,]    0    0 #>  [88,]    0    0 #>  [89,]    0    0 #>  [90,]    0    0 #>  [91,]    0    0 #>  [92,]    0    0 #>  [93,]    0    0 #>  [94,]    0    0 #>  [95,]    0    0 #>  [96,]    0    0 #>  [97,]    0    0 #>  [98,]    0    0 #>  [99,]    0    0 #> [100,]    0    0 #> [101,]    0    0 #> [102,]    0    0 #> [103,]    0    0 #> [104,]    0    0 #> [105,]    0    0 #> [106,]    0    0 #> [107,]    0    0 #> [108,]    0    0 #> [109,]    0    0 #> [110,]    0    0 #> [111,]    0    0 #> [112,]    0    0 #> [113,]    0    0 #> [114,]    0    0 #> [115,]    0    0 #> [116,]    0    0 #> [117,]    0    0 #> [118,]    0    0 #> [119,]    0    0 #> [120,]    0    0 #> [121,]    0    0 #> [122,]    0    0 #> [123,]    0    0 #> [124,]    0    0 #> [125,]    0    0 #> [126,]    0    0 #> [127,]    0    0 #> [128,]    0    0 #> [129,]    0    0 #> [130,]    0    0 #> [131,]    0    0 #> [132,]    0    0 #> [133,]    0    0 #> [134,]    0    0 #> [135,]    0    0 #> [136,]    0    0 #> [137,]    0    0 #> [138,]    0    0 #> [139,]    0    0 #> [140,]    0    0 #> [141,]    0    0 #> [142,]    0    0 #> [143,]    0    0 #> [144,]    0    0 #> [145,]    0    0 #> [146,]    0    0 #> [147,]    0    0 #> [148,]    0    0 #> [149,]    0    0 #> [150,]    0    0 #> [151,]    0    0 #> [152,]    0    0 #> [153,]    0    0 #> [154,]    0    0 #> [155,]    0    0 #> [156,]    0    0 #> [157,]    0    0 #> [158,]    0    0 #> [159,]    0    0 #> [160,]    0    0 #> [161,]    0    0 #> [162,]    0    0 #> [163,]    0    0 #> [164,]    0    0 #> [165,]    0    0 #> [166,]    0    0 #> [167,]    0    0 #> [168,]    0    0 #> [169,]    0    0 #> [170,]    0    0 #> [171,]    0    0 #> [172,]    0    0 #> [173,]    0    0 #> [174,]    0    0 #> [175,]    0    0 #> [176,]    0    0 #> [177,]    0    0 #> [178,]    0    0 #> [179,]    0    0 #> [180,]    0    0 #> [181,]    0    0 #> [182,]    0    0 #> [183,]    0    0 #> [184,]    0    0 #> [185,]    0    0 #> [186,]    0    0 #> [187,]    0    0 #> [188,]    0    0 #> [189,]    0    0 #> [190,]    0    0 #> [191,]    0    0 #> [192,]    0    0 #> [193,]    0    0 #> [194,]    0    0 #> [195,]    0    0 #> [196,]    0    0 #> [197,]    0    0 #> [198,]    0    0 #> [199,]    0    0 #> [200,]    0    0 #> [201,]    0    0 #> [202,]    0    0 #> [203,]    0    0 #> [204,]    0    0 #> [205,]    0    0 #> [206,]    0    0 #> [207,]    0    0 #> [208,]    0    0 #> [209,]    0    0 #> [210,]    0    0 #> [211,]    0    0 #> [212,]    0    0 #> [213,]    0    0 #> [214,]    0    0 #> [215,]    0    0 #> [216,]    0    0 #> [217,]    0    0 #> [218,]    0    0 #> [219,]    0    1 #> [220,]    1    0 #> [221,]    0    0 #> [222,]    0    0 #> [223,]    0    0 #> [224,]    0    0 #> [225,]    0    0 #> [226,]    0    0 #> [227,]    0    0 #> [228,]    0    0 #> [229,]    0    0 #> [230,]    0    0 #> [231,]    0    0 #> [232,]    0    0 #> [233,]    0    0 #> [234,]    0    0 #> [235,]    0    0 #> [236,]    0    0 #> [237,]    0    0 #> [238,]    0    0 #> [239,]    0    0 #> [240,]    0    0 #> [241,]    0    0 #> [242,]    0    0 #> [243,]    0    0 #> [244,]    0    0 #> [245,]    0    0 #> [246,]    0    0 #> [247,]    0    0 #> [248,]    0    0 #> [249,]    0    0 #> [250,]    0    0 #> [251,]    0    0 #> [252,]    0    0 #> [253,]    0    0 #> [254,]    0    0 #> [255,]    0    0 #> [256,]    0    0 #> [257,]    0    0 #> [258,]    0    0 #> [259,]    0    0 #> [260,]    0    0 #> [261,]    0    0 #> [262,]    0    0 #> [263,]    0    0 #> [264,]    0    0 #> [265,]    0    0 #> [266,]    0    0 #> [267,]    0    0 #> [268,]    0    0 #> [269,]    0    0 #> [270,]    0    0 #> [271,]    0    0 #> [272,]    0    0 #> [273,]    0    0 #> [274,]    0    0 #> [275,]    0    0 #> [276,]    0    0 #> [277,]    0    0 #> [278,]    0    0 #> [279,]    0    0 #> [280,]    0    0 #> [281,]    0    0 #> [282,]    0    0 #> [283,]    0    0 #> [284,]    0    0 #> [285,]    0    0 #> [286,]    0    0 #> [287,]    0    0 #> [288,]    0    0 #> [289,]    0    0 #> [290,]    0    0 #> [291,]    0    0 #> [292,]    0    0 #> [293,]    0    0 #> [294,]    0    0 #> [295,]    0    0 #> [296,]    0    0 #> [297,]    0    0 #> [298,]    0    0 #> [299,]    0    0 #> [300,]    0    0 #> [301,]    0    0 #> [302,]    0    0 #> [303,]    0    0 #> [304,]    0    0 #> [305,]    0    0 #> [306,]    0    0 #> [307,]    0    0 #> [308,]    0    0 #> [309,]    0    0 #> [310,]    0    0 #> [311,]    0    0 #> [312,]    0    0 #> [313,]    0    0 #> [314,]    0    0 #> [315,]    0    0 #> [316,]    0    0 #> [317,]    0    0 #> [318,]    0    0 #> [319,]    0    0 #> [320,]    0    0 #> [321,]    0    0 #> [322,]    0    0 #> [323,]    0    0 #> [324,]    0    0 #> [325,]    0    0 #> [326,]    0    0 #> [327,]    0    0 #> [328,]    0    0 #> [329,]    0    0 #> [330,]    0    0 #> [331,]    0    0 #> [332,]    0    0 #> [333,]    0    0 #> [334,]    0    0 #> [335,]    0    0 #> [336,]    0    0 #> [337,]    0    0 #> [338,]    0    0 #> [339,]    0    0 #> [340,]    0    0 #> [341,]    0    0 #> [342,]    0    0 #> [343,]    0    0 #> [344,]    0    0 #> [345,]    0    0 #> [346,]    0    0 #> [347,]    0    0 #> [348,]    0    0 #> [349,]    0    0 #> [350,]    0    0 #> [351,]    0    0 #> [352,]    0    0 #> [353,]    0    0 #> [354,]    0    0 #> [355,]    0    0 #> [356,]    0    0 #> [357,]    0    0 #> [358,]    0    0 #> [359,]    0    0 #> [360,]    0    0 #> [361,]    0    0 #> [362,]    0    0 #> [363,]    0    0 #> [364,]    0    0 #> [365,]    0    0 #> [366,]    0    0 #> [367,]    0    0 #> [368,]    0    0 #> [369,]    0    0 #> [370,]    0    0 #> [371,]    0    0 #> [372,]    0    0 #> [373,]    0    0 #> [374,]    0    0 #> [375,]    0    0 #> [376,]    0    0 #> [377,]    0    0 #> [378,]    0    0 #> [379,]    0    0 #> [380,]    0    0 #> [381,]    0    0 #> [382,]    0    0 #> [383,]    0    0 #> [384,]    0    0 #> [385,]    0    0 #> [386,]    0    0 #> [387,]    0    0 #> [388,]    0    0 #> [389,]    0    0 #> [390,]    0    0 #> [391,]    0    0 #> [392,]    0    0 #> [393,]    0    0 #> [394,]    0    0 #> [395,]    0    0 #> [396,]    0    0 #> [397,]    0    0 #> [398,]    0    0 #> [399,]    0    0 #> [400,]    0    0 #> [401,]    0    0 #> [402,]    0    0 #> [403,]    0    0 #> [404,]    0    0 #> [405,]    0    0 #> [406,]    0    0 #> [407,]    0    0 #> [408,]    0    0 #> [409,]    0    0 #> [410,]    0    0 #> [411,]    0    0 #> [412,]    0    0 #> [413,]    0    0 #> [414,]    0    0 #> [415,]    0    0 #> [416,]    0    0 #> [417,]    0    0 #> [418,]    0    0 #> [419,]    0    0 #> [420,]    0    0 #> [421,]    0    0 #> [422,]    0    0 #> [423,]    0    0 #> [424,]    0    0 #> [425,]    0    0 #>  #> $model$b #> [1] -211.0035  189.6218 #>  #> $model$bcov #>           [,1]      [,2] #> [1,] 1516.9191  124.4537 #> [2,]  124.4537 1516.9191 #>  #> $model$linearized #>   [1]  460.1000  502.6000  443.8000  459.1000  438.4000  465.1000  452.7000 #>   [8]  522.9000  889.3000  379.2000  378.0000  472.1000  503.4000  510.6000 #>  [15]  462.4000  468.3000  458.2000  482.7000  485.3000  568.7000  963.7000 #>  [22]  414.7000  414.5000  484.7000  487.3000  597.9000  500.4000  543.4000 #>  [29]  503.4000  522.8000  556.6000  623.2000 1039.4000  516.3000  452.5000 #>  [36]  525.8000  587.7000  700.3000  561.8000  602.8000  582.5000  563.1000 #>  [43]  637.1000  697.1000 1187.5000  570.5000  478.2000  547.4000  594.3000 #>  [50]  751.6000  553.4000  663.2000  581.1000  661.9000  665.6000  700.9000 #>  [57] 1367.9000  613.9000  513.2000  599.9000  674.1000  714.0000  670.5000 #>  [64]  720.9000  601.6000  672.3000  709.1000  743.2000 1460.1000  631.0000 #>  [71]  551.1000  678.1000  715.7000  740.8000  722.0000  683.5000  650.9000 #>  [78]  723.3000  729.6000  870.3000 1570.0000  631.5000  552.0000  719.0000 #>  [85]  697.6000  764.8000  786.3000  715.1000  723.8000  757.9000  751.7000 #>  [92]  923.8000 1569.4000  678.2000  586.2000  726.8000  744.1000  815.5000 #>  [99]  832.4000  710.3000  759.4000  741.1000  786.6000  931.5000 1563.1000 #> [106]  694.0000  604.7000  719.2000  748.2000  828.2000  746.9000  794.5000 #> [113]  770.4000  741.5000  858.6000  944.7000 1600.3000  740.0000  665.9000 #> [120]  701.5000  831.4000  878.6000  826.0000  788.2000  723.6000  819.8000 #> [127]  902.5000  968.6000 1650.9000  762.1000  643.0000  754.1000  840.7000 #> [134]  906.6000  887.1000  771.5000  728.7000  844.7000  886.9000  970.0000 #> [141] 1710.5000  745.7000  664.4000  821.5000  831.7000  908.0000  912.6000 #> [148]  782.9000  798.8000  887.0000  934.6000 1000.4000 1817.5000  752.4000 #> [155]  682.5000  811.2000  906.0000  927.2000  906.8000  880.6000  873.9000 #> [162]  856.8000  920.6000 1067.4000 1857.2000  833.1000  737.1000  812.0000 #> [169]  895.2000  962.8000  908.6000  908.0000  888.9000  833.7000  933.7000 #> [176] 1081.6000 1837.6000  840.9000  727.4000  857.9000  849.0000  994.8000 #> [183]  830.2000  971.1000  836.0000  939.1000  976.9000 1111.3000 1879.1000 #> [190]  917.3000  716.2000  822.9000  970.1000  970.2000  849.4000 1042.3000 #> [197]  869.9000  939.4000 1021.3000 1137.7000 1975.7000  942.0000  738.4000 #> [204]  903.2000  953.2000 1011.2000  894.4000 1054.5000  899.5000 1002.3000 #> [211] 1043.7000 1207.2000 2069.6000  924.9000  798.2000  901.9000 1024.7000 #> [218] 1052.3000  975.8782 1070.3035 1009.2000 1054.6000 1070.4000 1232.5000 #> [225] 2177.5000  971.9000  814.6000 1017.5000 1039.2000 1123.5000 1024.9000 #> [232] 1100.8000  963.0000 1012.9000 1132.0000 1344.8000 2269.5000 1027.9000 #> [239]  841.4000 1043.9000 1075.3000 1190.9000 1143.0000 1075.7000 1065.9000 #> [246] 1060.1000 1211.4000 1495.1000 2338.6000 1099.3000  900.5000 1092.7000 #> [253] 1222.4000 1237.1000 1237.9000 1182.0000 1101.2000 1198.2000 1316.1000 #> [260] 1528.2000 2424.2000 1182.9000  989.8000 1131.4000 1277.1000 1280.3000 #> [267] 1384.1000 1305.9000 1166.8000 1317.9000 1358.3000 1536.7000 2500.8000 #> [274] 1246.3000 1037.3000 1300.8000 1153.7000 1264.2000 1454.2000 1290.1000 #> [281] 1210.7000 1277.8000 1314.4000 1540.4000 2536.0000 1193.7000 1037.7000 #> [288] 1204.5000 1348.6000 1267.6000 1429.0000 1412.0000 1239.2000 1219.1000 #> [295] 1344.6000 1623.3000 2611.1000 1267.3000 1047.0000 1331.6000 1302.6000 #> [302] 1365.1000 1491.5000 1462.3000 1315.5000 1353.3000 1440.6000 1687.9000 #> [309] 2747.0000 1397.8000 1140.5000 1351.7000 1396.6000 1421.1000 1401.6000 #> [316] 1582.3000 1268.4000 1383.3000 1452.4000 1675.9000 2886.1000 1451.0000 #> [323] 1056.6000 1386.9000 1509.1000 1519.4000 1500.5000 1570.7000 1341.5000 #> [330] 1399.9000 1534.3000 1736.6000 2795.1000 1469.1000 1111.9000 1379.9000 #> [337] 1389.7000 1427.2000 1551.4000 1581.0000 1324.0000 1422.0000 1464.9000 #> [344] 1705.5000 2752.4000 1412.6000 1117.5000 1321.6000 1472.6000 1408.9000 #> [351] 1471.9000 1532.5000 1293.5000 1345.7000 1404.7000 1660.0000 2730.5000 #> [358] 1362.4000 1131.7000 1349.2000 1391.2000 1456.9000 1616.4000 1423.4000 #> [365] 1359.0000 1367.8000 1442.6000 1672.9000 2753.3000 1397.4000 1113.6000 #> [372] 1397.3000 1339.1000 1441.9000 1537.4000 1390.6000 1337.2000 1359.4000 #> [379] 1463.3000 1668.9000 2725.5000 1451.0000 1064.9000 1293.2000 1442.9000 #> [386] 1411.8000 1461.6000 1501.6000 1254.2000 1356.4000 1478.7000 1687.7000 #> [393] 2756.9000 1471.2000 1053.8000 1367.2000 1442.2000 1428.7000 1480.9000 #> [400] 1540.9000 1331.9000 1400.1000 1566.3000 1730.5000 2913.6000 1519.2000 #> [407] 1155.8000 1451.5000 1451.0000 1449.7000 1596.1000 1468.3000 1293.9000 #> [414] 1393.5000 1497.4000 1684.3000 2850.4000 1428.5000 1092.4000 1370.3000 #> [421] 1522.6000 1452.4000 1557.2000 1445.5000 1303.1000 #>  #>  #> $likelihood #> $likelihood$initial #> $likelihood$initial$ll #> [1] -2218.964 #>  #> $likelihood$initial$ssq #> [1] 1139531 #>  #> $likelihood$initial$nobs #> [1] 425 #>  #> $likelihood$initial$neffective #> [1] -1 #>  #> $likelihood$initial$nparams #> [1] 3 #>  #> $likelihood$initial$df #> [1] 409 #>  #> $likelihood$initial$aic #> [1] 4443.928 #>  #> $likelihood$initial$aicc #> [1] 4443.987 #>  #> $likelihood$initial$bic #> [1] 4455.991 #>  #> $likelihood$initial$bic2 #> [1] 10.81551 #>  #> $likelihood$initial$bicc #> [1] 7.954332 #>  #> $likelihood$initial$hannanquinn #> [1] 4448.7 #>  #>  #> $likelihood$final #> $likelihood$final$ll #> [1] -2194.499 #>  #> $likelihood$final$ssq #> [1] 1014171 #>  #> $likelihood$final$nobs #> [1] 425 #>  #> $likelihood$final$neffective #> [1] -1 #>  #> $likelihood$final$nparams #> [1] 3 #>  #> $likelihood$final$df #> [1] 409 #>  #> $likelihood$final$aic #> [1] 4394.998 #>  #> $likelihood$final$aicc #> [1] 4395.057 #>  #> $likelihood$final$bic #> [1] 4407.061 #>  #> $likelihood$final$bic2 #> [1] 10.69675 #>  #> $likelihood$final$bicc #> [1] 7.837787 #>  #> $likelihood$final$hannanquinn #> [1] 4399.77 #>  #>  #>  #> attr(,\"class\") #> [1] \"JD3_REGARIMA_OUTLIERS\""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"Seasonal Adjustment  TRAMO-SEATS","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"","code":"tramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  tramoseats_fast(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  jtramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context dictionnary variables. userdefined vector containing additional output variables (see tramoseats_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"tramoseats() function returns list results, estimation specification result specification, tramoseats_fast() faster function returns results. jtramoseats() functions results java object custom outputs packages (use rjd3toolkit::dictionary() get list variables rjd3toolkit::result() get specific variable).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"","code":"library(\"rjd3toolkit\") sp = tramoseats_spec(\"rsafull\") y = rjd3toolkit::ABS$X0.2.09.10.M tramoseats_fast(y, spec = sp) #> TRAMO #> Log-transformation: yes  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error  T-stat #> theta(1)  -0.82783    0.02571 -32.196 #> btheta(1) -0.42554    0.06388  -6.661 #>  #> Regression model: #>                   Estimate Std. Error T-stat #> monday          -0.0109446  0.0034805 -3.145 #> tuesday          0.0048940  0.0035307  1.386 #> wednesday        0.0001761  0.0034970  0.050 #> thursday         0.0132928  0.0035330  3.763 #> friday          -0.0024801  0.0035383 -0.701 #> saturday         0.0153509  0.0035171  4.365 #> lp               0.0410667  0.0101178  4.059 #> easter           0.0503888  0.0072698  6.931 #> AO (2000-06-01)  0.1681662  0.0299743  5.610 #> AO (2000-07-01) -0.1972348  0.0298664 -6.604 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  13  #>  #> Loglikelihood:  781.358  #> Adjusted loglikelihood:  -2086.269  #>  #> Standard error of the regression (ML estimate):  0.03615788  #> AIC:  4198.538  #> AICC:  4199.452  #> BIC:  4250.811  #>  #>  #> Decomposition #> model  #>  #> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1  #> MA:  1 -0.8278316 0 0 0 0 0 0 0 0 0 0 -0.4255409 0.3522762  #> var:  1  #>  #> trend  #>  #> DIF:  1 -2 1  #> MA:  1 0.06437327 -0.9356267  #> var:  0.004098184  #>  #> seasonal  #>  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.6197472 0.3121522 0.07325877 -0.1026014 -0.2222985 -0.2934508 -0.3240296 -0.3220203 -0.295141 -0.2506158 -0.1950007  #> var:  0.1460322  #>  #> irregular  #>  #> var:  0.384587  #>  #>  #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #>  #>            Component #>  cycle         0.250 #>  seasonal     97.046 #>  irregular     0.600 #>  calendar      0.741 #>  others        0.325 #>  total        98.962 #>  #> Residual seasonality tests #>                 P.value #>  seas.ftest.i         1 #>  seas.ftest.sa        1 #>  seas.qstest.i        1 #>  seas.qstest.sa       1 #>  td.ftest.i           1 #>  td.ftest.sa          1 #>  #>  #> Final #> Last values #>          series       sa    trend      seas       irr #> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979 #> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681 #> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508 #> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588 #> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587 #> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303 #> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913 #> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187 #> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924 #> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726 #> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781 #> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960 sp = add_outlier(sp,                  type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) sp = set_transform(   set_tradingdays(     set_easter(sp, enabled = FALSE),     option = \"workingdays\"   ),   fun = \"None\" ) tramoseats_fast(y, spec = sp) #> TRAMO #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>  #>  #> Decomposition #> model  #>  #> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1  #> MA:  1 -0.8235461 0 0 0 0 0 0 0 0 0 0 -0.2608057 0.2147855  #> var:  1  #>  #> trend  #>  #> DIF:  1 -2 1  #> MA:  1 0.09197092 -0.9080291  #> var:  0.003491855  #>  #> seasonal  #>  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.6220678 0.315157 0.07593327 -0.1008043 -0.2215881 -0.2938052 -0.3252816 -0.3239249 -0.2974269 -0.2530226 -0.1973046  #> var:  0.240472  #>  #> irregular  #>  #> var:  0.2654024  #>  #>  #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #>  #>            Component #>  cycle         0.198 #>  seasonal     98.409 #>  irregular     0.223 #>  calendar      0.246 #>  others        0.257 #>  total        99.333 #>  #> Residual seasonality tests #>                 P.value #>  seas.ftest.i         1 #>  seas.ftest.sa        1 #>  seas.qstest.i        1 #>  seas.qstest.sa       1 #>  td.ftest.i           1 #>  td.ftest.sa          1 #>  #>  #> Final #> Last values #>          series       sa    trend        seas          irr #> Sep 2016 1393.5 1557.498 1559.704 -163.997649  -2.20658744 #> Oct 2016 1497.4 1553.718 1557.296  -56.318379  -3.57749836 #> Nov 2016 1684.3 1538.837 1555.164  145.462939 -16.32645755 #> Dec 2016 2850.4 1544.533 1553.556 1305.866600  -9.02221292 #> Jan 2017 1428.5 1540.203 1552.499 -111.703036 -12.29569819 #> Feb 2017 1092.4 1547.827 1551.890 -455.426856  -4.06311664 #> Mar 2017 1370.3 1535.240 1551.720 -164.939565 -16.48081320 #> Apr 2017 1522.6 1581.396 1551.632  -58.795692  29.76367672 #> May 2017 1452.4 1552.527 1551.001 -100.127238   1.52621793 #> Jun 2017 1557.2 1549.996 1549.968    7.204385   0.02775996 #> Jul 2017 1445.5 1543.239 1548.979  -97.739206  -5.73936138 #> Aug 2017 1303.1 1544.526 1548.167 -241.425886  -3.64143843"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO-SEATS Dictionary — tramoseats_dictionary","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"TRAMO-SEATS Dictionary","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"","code":"tramoseats_dictionary()"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"vector containing names available output objects (series, diagnostics, parameters).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":null,"dir":"Reference","previous_headings":"","what":"Revisions History — tramoseats_revisions","title":"Revisions History — tramoseats_revisions","text":"Compute revisions history","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Revisions History — tramoseats_revisions","text":"","code":"tramoseats_revisions(   ts,   spec,   data_ids = NULL,   ts_ids = NULL,   cmp_ids = NULL,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Revisions History — tramoseats_revisions","text":"ts time series used estimation. spec specification used. data_ids list list specify statistics export. sub-list must contain two elements: start (first date compute history, format \"YYYY-MM-DD\") id (name statistics, see tramoseats_dictionary()). See example. ts_ids list list specify specific date component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), period (date studied) id (name component, see tramoseats_dictionary()). See example. cmp_ids list list specify component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), end (last date compute history, format \"YYYY-MM-DD\") id (name component, see tramoseats_dictionary()). many series periods start end exported. See example. context context specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Revisions History — tramoseats_revisions","text":"","code":"s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- tramoseats(s) data_ids <- list(   # Get the coefficient of the trading-day coefficient from 2005-jan   list(start = \"2005-01-01\", id = \"regression.td(1)\"),   # Get the ljung-box statistics on residuals from 2010-jan   list(start = \"2010-01-01\", id = \"residuals.lb\")) ts_ids <- list(   # Get the SA component estimates of 2010-jan from 2010-jan   list(period = \"2010-01-01\", start = \"2010-01-01\", id = \"sa\"),   # Get the irregular component estimates of 2010-jan from 2015-jan   list(period = \"2010-01-01\", start = \"2015-01-01\", id = \"i\")) cmp_ids <- list(   # Get the SA component estimates (full time series) 2010-jan to 2020-jan   list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"sa\"),   # Get the trend component estimates (full time series)  2010-jan to 2020-jan   list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"t\")) rh <- tramoseats_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"Set functions(tramoseats_spec(),tramo_spec()) create default specifications associated TRAMO-SEATS seasonal adjustment method. Specification creation can restricted tramo part tramo_spec() function.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"","code":"tramo_spec(name = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"))  tramoseats_spec(   name = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\") )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"name name predefined specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"object class \"JD3_TRAMOSEATS_SPEC\" (tramoseats_spec()) \"JD3_TRAMO_SPEC\" (tramo_spec()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"Without argument tramo_spec() yields TR5 specification without argument tramoseats_spec()  yields RSA5 specification available predefined 'JDemetra+' model specifications described table :","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"","code":"init_spec <- tramoseats_spec() init_spec <- tramo_spec() init_spec <- tramoseats_spec(\"rsa3\") init_spec <- tramo_spec(\"tr3\")"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"Function generating comprehensive list available output variables estimation process  tramo() tramoseats() functions. items available default estimation output remainder can added using userdefined parameter. User-defined objects can retrieved list lists generated estimation process","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"","code":"userdefined_variables_tramoseats(x = c(\"TRAMO-SEATS\", \"TRAMO\"))"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"x character indicate estimation function output items list displayed.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"vector containing names available output objects (series, diagnostics, parameters)","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"","code":"userdefined_variables_tramoseats(\"tramoseats\") #>   [1] \"adjust\"                               #>   [2] \"arima.bd\"                             #>   [3] \"arima.bp\"                             #>   [4] \"arima.bphi(*)\"                        #>   [5] \"arima.bq\"                             #>   [6] \"arima.btheta(*)\"                      #>   [7] \"arima.d\"                              #>   [8] \"arima.p\"                              #>   [9] \"arima.phi(*)\"                         #>  [10] \"arima.q\"                              #>  [11] \"arima.theta(*)\"                       #>  [12] \"benchmarking.original\"                #>  [13] \"benchmarking.result\"                  #>  [14] \"benchmarking.target\"                  #>  [15] \"cal\"                                  #>  [16] \"cal_b\"                                #>  [17] \"cal_b(?)\"                             #>  [18] \"cal_f\"                                #>  [19] \"cal_f(?)\"                             #>  [20] \"decomposition.finalmodel.ac\"          #>  [21] \"decomposition.finalmodel.bd\"          #>  [22] \"decomposition.finalmodel.bp\"          #>  [23] \"decomposition.finalmodel.bphi(*)\"     #>  [24] \"decomposition.finalmodel.bq\"          #>  [25] \"decomposition.finalmodel.btheta(*)\"   #>  [26] \"decomposition.finalmodel.d\"           #>  [27] \"decomposition.finalmodel.p\"           #>  [28] \"decomposition.finalmodel.phi(*)\"      #>  [29] \"decomposition.finalmodel.q\"           #>  [30] \"decomposition.finalmodel.spectrum\"    #>  [31] \"decomposition.finalmodel.theta(*)\"    #>  [32] \"decomposition.i_cmp\"                  #>  [33] \"decomposition.i_cmp_b\"                #>  [34] \"decomposition.i_cmp_eb\"               #>  [35] \"decomposition.i_cmp_ef\"               #>  [36] \"decomposition.i_cmp_f\"                #>  [37] \"decomposition.i_lin\"                  #>  [38] \"decomposition.i_lin_b\"                #>  [39] \"decomposition.i_lin_e\"                #>  [40] \"decomposition.i_lin_eb\"               #>  [41] \"decomposition.i_lin_ef\"               #>  [42] \"decomposition.i_lin_f\"                #>  [43] \"decomposition.initialmodel.ac\"        #>  [44] \"decomposition.initialmodel.bd\"        #>  [45] \"decomposition.initialmodel.bp\"        #>  [46] \"decomposition.initialmodel.bphi(*)\"   #>  [47] \"decomposition.initialmodel.bq\"        #>  [48] \"decomposition.initialmodel.btheta(*)\" #>  [49] \"decomposition.initialmodel.d\"         #>  [50] \"decomposition.initialmodel.p\"         #>  [51] \"decomposition.initialmodel.phi(*)\"    #>  [52] \"decomposition.initialmodel.q\"         #>  [53] \"decomposition.initialmodel.spectrum\"  #>  [54] \"decomposition.initialmodel.theta(*)\"  #>  [55] \"decomposition.s_cmp\"                  #>  [56] \"decomposition.s_cmp_b\"                #>  [57] \"decomposition.s_cmp_eb\"               #>  [58] \"decomposition.s_cmp_ef\"               #>  [59] \"decomposition.s_cmp_f\"                #>  [60] \"decomposition.s_lin\"                  #>  [61] \"decomposition.s_lin_b\"                #>  [62] \"decomposition.s_lin_e\"                #>  [63] \"decomposition.s_lin_eb\"               #>  [64] \"decomposition.s_lin_ef\"               #>  [65] \"decomposition.s_lin_f\"                #>  [66] \"decomposition.sa_cmp\"                 #>  [67] \"decomposition.sa_cmp_b\"               #>  [68] \"decomposition.sa_cmp_eb\"              #>  [69] \"decomposition.sa_cmp_ef\"              #>  [70] \"decomposition.sa_cmp_f\"               #>  [71] \"decomposition.sa_lin\"                 #>  [72] \"decomposition.sa_lin_b\"               #>  [73] \"decomposition.sa_lin_e\"               #>  [74] \"decomposition.sa_lin_eb\"              #>  [75] \"decomposition.sa_lin_ef\"              #>  [76] \"decomposition.sa_lin_f\"               #>  [77] \"decomposition.t_cmp\"                  #>  [78] \"decomposition.t_cmp_b\"                #>  [79] \"decomposition.t_cmp_eb\"               #>  [80] \"decomposition.t_cmp_ef\"               #>  [81] \"decomposition.t_cmp_f\"                #>  [82] \"decomposition.t_lin\"                  #>  [83] \"decomposition.t_lin_b\"                #>  [84] \"decomposition.t_lin_e\"                #>  [85] \"decomposition.t_lin_eb\"               #>  [86] \"decomposition.t_lin_ef\"               #>  [87] \"decomposition.t_lin_f\"                #>  [88] \"decomposition.y_cmp\"                  #>  [89] \"decomposition.y_cmp_b\"                #>  [90] \"decomposition.y_cmp_eb\"               #>  [91] \"decomposition.y_cmp_ef\"               #>  [92] \"decomposition.y_cmp_f\"                #>  [93] \"decomposition.y_lin\"                  #>  [94] \"decomposition.y_lin_b\"                #>  [95] \"decomposition.y_lin_eb\"               #>  [96] \"decomposition.y_lin_ef\"               #>  [97] \"decomposition.y_lin_f\"                #>  [98] \"det\"                                  #>  [99] \"det_b(?)\"                             #> [100] \"det_f(?)\"                             #> [101] \"det_i\"                                #> [102] \"det_i_b(?)\"                           #> [103] \"det_i_f(?)\"                           #> [104] \"det_s\"                                #> [105] \"det_s_b(?)\"                           #> [106] \"det_s_f(?)\"                           #> [107] \"det_t\"                                #> [108] \"det_t_b(?)\"                           #> [109] \"det_t_f(?)\"                           #> [110] \"diagnostics.fcast-insample-mean\"      #> [111] \"diagnostics.fcast-outsample-mean\"     #> [112] \"diagnostics.fcast-outsample-variance\" #> [113] \"diagnostics.seas-i-combined\"          #> [114] \"diagnostics.seas-i-combined3\"         #> [115] \"diagnostics.seas-i-evolutive\"         #> [116] \"diagnostics.seas-i-f\"                 #> [117] \"diagnostics.seas-i-friedman\"          #> [118] \"diagnostics.seas-i-kw\"                #> [119] \"diagnostics.seas-i-periodogram\"       #> [120] \"diagnostics.seas-i-qs\"                #> [121] \"diagnostics.seas-i-spectralpeaks\"     #> [122] \"diagnostics.seas-i-stable\"            #> [123] \"diagnostics.seas-lin-combined\"        #> [124] \"diagnostics.seas-lin-evolutive\"       #> [125] \"diagnostics.seas-lin-f\"               #> [126] \"diagnostics.seas-lin-friedman\"        #> [127] \"diagnostics.seas-lin-kw\"              #> [128] \"diagnostics.seas-lin-periodogram\"     #> [129] \"diagnostics.seas-lin-qs\"              #> [130] \"diagnostics.seas-lin-spectralpeaks\"   #> [131] \"diagnostics.seas-lin-stable\"          #> [132] \"diagnostics.seas-res-combined\"        #> [133] \"diagnostics.seas-res-combined3\"       #> [134] \"diagnostics.seas-res-evolutive\"       #> [135] \"diagnostics.seas-res-f\"               #> [136] \"diagnostics.seas-res-friedman\"        #> [137] \"diagnostics.seas-res-kw\"              #> [138] \"diagnostics.seas-res-periodogram\"     #> [139] \"diagnostics.seas-res-qs\"              #> [140] \"diagnostics.seas-res-spectralpeaks\"   #> [141] \"diagnostics.seas-res-stable\"          #> [142] \"diagnostics.seas-sa-ac1\"              #> [143] \"diagnostics.seas-sa-combined\"         #> [144] \"diagnostics.seas-sa-combined3\"        #> [145] \"diagnostics.seas-sa-evolutive\"        #> [146] \"diagnostics.seas-sa-f\"                #> [147] \"diagnostics.seas-sa-friedman\"         #> [148] \"diagnostics.seas-sa-kw\"               #> [149] \"diagnostics.seas-sa-periodogram\"      #> [150] \"diagnostics.seas-sa-qs\"               #> [151] \"diagnostics.seas-sa-spectralpeaks\"    #> [152] \"diagnostics.seas-sa-stable\"           #> [153] \"diagnostics.seas-si-combined\"         #> [154] \"diagnostics.seas-si-combined3\"        #> [155] \"diagnostics.seas-si-evolutive\"        #> [156] \"diagnostics.seas-si-stable\"           #> [157] \"diagnostics.td-i-all\"                 #> [158] \"diagnostics.td-i-last\"                #> [159] \"diagnostics.td-res-all\"               #> [160] \"diagnostics.td-res-last\"              #> [161] \"diagnostics.td-sa-all\"                #> [162] \"diagnostics.td-sa-last\"               #> [163] \"ee\"                                   #> [164] \"ee_b(?)\"                              #> [165] \"ee_f(?)\"                              #> [166] \"i\"                                    #> [167] \"i_b\"                                  #> [168] \"i_eb\"                                 #> [169] \"i_ef\"                                 #> [170] \"i_f\"                                  #> [171] \"l\"                                    #> [172] \"likelihood.adjustedll\"                #> [173] \"likelihood.aic\"                       #> [174] \"likelihood.aicc\"                      #> [175] \"likelihood.bic\"                       #> [176] \"likelihood.bic2\"                      #> [177] \"likelihood.bicc\"                      #> [178] \"likelihood.df\"                        #> [179] \"likelihood.hannanquinn\"               #> [180] \"likelihood.ll\"                        #> [181] \"likelihood.neffectiveobs\"             #> [182] \"likelihood.nobs\"                      #> [183] \"likelihood.nparams\"                   #> [184] \"likelihood.ssqerr\"                    #> [185] \"log\"                                  #> [186] \"mhe\"                                  #> [187] \"mhe_b(?)\"                             #> [188] \"mhe_f(?)\"                             #> [189] \"mode\"                                 #> [190] \"omhe\"                                 #> [191] \"omhe_b(?)\"                            #> [192] \"omhe_f(?)\"                            #> [193] \"out\"                                  #> [194] \"out_b(?)\"                             #> [195] \"out_f(?)\"                             #> [196] \"out_i\"                                #> [197] \"out_i_b(?)\"                           #> [198] \"out_i_f(?)\"                           #> [199] \"out_s\"                                #> [200] \"out_s_b(?)\"                           #> [201] \"out_s_f(?)\"                           #> [202] \"out_t\"                                #> [203] \"out_t_b(?)\"                           #> [204] \"out_t_f(?)\"                           #> [205] \"period\"                               #> [206] \"reg_i\"                                #> [207] \"reg_i_b(?)\"                           #> [208] \"reg_i_f(?)\"                           #> [209] \"reg_s\"                                #> [210] \"reg_s_b(?)\"                           #> [211] \"reg_s_f(?)\"                           #> [212] \"reg_sa\"                               #> [213] \"reg_sa_b(?)\"                          #> [214] \"reg_sa_f(?)\"                          #> [215] \"reg_t\"                                #> [216] \"reg_t_b(?)\"                           #> [217] \"reg_t_f(?)\"                           #> [218] \"reg_u\"                                #> [219] \"reg_u_b(?)\"                           #> [220] \"reg_u_f(?)\"                           #> [221] \"reg_y\"                                #> [222] \"reg_y_b(?)\"                           #> [223] \"reg_y_f(?)\"                           #> [224] \"regression.description\"               #> [225] \"regression.details.coefficients\"      #> [226] \"regression.details.covar\"             #> [227] \"regression.details.covar-ml\"          #> [228] \"regression.easter\"                    #> [229] \"regression.espan.end\"                 #> [230] \"regression.espan.missing\"             #> [231] \"regression.espan.n\"                   #> [232] \"regression.espan.start\"               #> [233] \"regression.leaster\"                   #> [234] \"regression.lp\"                        #> [235] \"regression.mean\"                      #> [236] \"regression.missing(*)\"                #> [237] \"regression.ml.parameters\"             #> [238] \"regression.ml.pcorr\"                  #> [239] \"regression.ml.pcovar\"                 #> [240] \"regression.ml.pcovar-ml\"              #> [241] \"regression.ml.pscore\"                 #> [242] \"regression.mu\"                        #> [243] \"regression.nao\"                       #> [244] \"regression.nlp\"                       #> [245] \"regression.nls\"                       #> [246] \"regression.nmh\"                       #> [247] \"regression.nout\"                      #> [248] \"regression.nso\"                       #> [249] \"regression.ntc\"                       #> [250] \"regression.ntd\"                       #> [251] \"regression.nusers\"                    #> [252] \"regression.out(*)\"                    #> [253] \"regression.outlier(*)\"                #> [254] \"regression.td(*)\"                     #> [255] \"regression.type\"                      #> [256] \"regression.user(*)\"                   #> [257] \"residuals.bp\"                         #> [258] \"residuals.bp2\"                        #> [259] \"residuals.doornikhansen\"              #> [260] \"residuals.kurtosis\"                   #> [261] \"residuals.lb\"                         #> [262] \"residuals.lb2\"                        #> [263] \"residuals.lruns\"                      #> [264] \"residuals.ludruns\"                    #> [265] \"residuals.mean\"                       #> [266] \"residuals.nruns\"                      #> [267] \"residuals.nudruns\"                    #> [268] \"residuals.res\"                        #> [269] \"residuals.seasbp\"                     #> [270] \"residuals.seaslb\"                     #> [271] \"residuals.ser\"                        #> [272] \"residuals.skewness\"                   #> [273] \"residuals.tsres\"                      #> [274] \"residuals.type\"                       #> [275] \"s\"                                    #> [276] \"s_b\"                                  #> [277] \"s_eb\"                                 #> [278] \"s_ef\"                                 #> [279] \"s_f\"                                  #> [280] \"sa\"                                   #> [281] \"sa_b\"                                 #> [282] \"sa_eb\"                                #> [283] \"sa_ef\"                                #> [284] \"sa_f\"                                 #> [285] \"seasonal\"                             #> [286] \"seats.ar_root(*)\"                     #> [287] \"seats.ivar-estimate\"                  #> [288] \"seats.ivar-estimator\"                 #> [289] \"seats.ivar-pvalue\"                    #> [290] \"seats.ma_root(*)\"                     #> [291] \"seats.model_changed\"                  #> [292] \"seats.parameters_cutoff\"              #> [293] \"seats.savar-estimate\"                 #> [294] \"seats.savar-estimator\"                #> [295] \"seats.savar-pvalue\"                   #> [296] \"seats.seasonality\"                    #> [297] \"seats.sicorr-estimate\"                #> [298] \"seats.sicorr-estimator\"               #> [299] \"seats.sicorr-pvalue\"                  #> [300] \"seats.svar-estimate\"                  #> [301] \"seats.svar-estimator\"                 #> [302] \"seats.svar-pvalue\"                    #> [303] \"seats.ticorr-estimate\"                #> [304] \"seats.ticorr-estimator\"               #> [305] \"seats.ticorr-pvalue\"                  #> [306] \"seats.tscorr-estimate\"                #> [307] \"seats.tscorr-estimator\"               #> [308] \"seats.tscorr-pvalue\"                  #> [309] \"seats.tvar-estimate\"                  #> [310] \"seats.tvar-estimator\"                 #> [311] \"seats.tvar-pvalue\"                    #> [312] \"span.end\"                             #> [313] \"span.missing\"                         #> [314] \"span.n\"                               #> [315] \"span.start\"                           #> [316] \"t\"                                    #> [317] \"t_b\"                                  #> [318] \"t_eb\"                                 #> [319] \"t_ef\"                                 #> [320] \"t_f\"                                  #> [321] \"tde\"                                  #> [322] \"tde_b(?)\"                             #> [323] \"tde_f(?)\"                             #> [324] \"variancedecomposition.cycle\"          #> [325] \"variancedecomposition.irregular\"      #> [326] \"variancedecomposition.others\"         #> [327] \"variancedecomposition.seasonality\"    #> [328] \"variancedecomposition.tdh\"            #> [329] \"variancedecomposition.total\"          #> [330] \"y\"                                    #> [331] \"y_b\"                                  #> [332] \"y_b(?)\"                               #> [333] \"y_eb(?)\"                              #> [334] \"y_ef(?)\"                              #> [335] \"y_f\"                                  #> [336] \"y_f(?)\"                               #> [337] \"yc\"                                   #> [338] \"ycal\"                                 #> [339] \"ycal_f(?)\"                            userdefined_variables_tramoseats(\"tramo\") #>   [1] \"adjust\"                          \"arima.bd\"                        #>   [3] \"arima.bp\"                        \"arima.bphi(*)\"                   #>   [5] \"arima.bq\"                        \"arima.btheta(*)\"                 #>   [7] \"arima.d\"                         \"arima.p\"                         #>   [9] \"arima.phi(*)\"                    \"arima.q\"                         #>  [11] \"arima.theta(*)\"                  \"cal\"                             #>  [13] \"cal_b(?)\"                        \"cal_f(?)\"                        #>  [15] \"det\"                             \"det_b(?)\"                        #>  [17] \"det_f(?)\"                        \"det_i\"                           #>  [19] \"det_i_b(?)\"                      \"det_i_f(?)\"                      #>  [21] \"det_s\"                           \"det_s_b(?)\"                      #>  [23] \"det_s_f(?)\"                      \"det_t\"                           #>  [25] \"det_t_b(?)\"                      \"det_t_f(?)\"                      #>  [27] \"ee\"                              \"ee_b(?)\"                         #>  [29] \"ee_f(?)\"                         \"l\"                               #>  [31] \"likelihood.adjustedll\"           \"likelihood.aic\"                  #>  [33] \"likelihood.aicc\"                 \"likelihood.bic\"                  #>  [35] \"likelihood.bic2\"                 \"likelihood.bicc\"                 #>  [37] \"likelihood.df\"                   \"likelihood.hannanquinn\"          #>  [39] \"likelihood.ll\"                   \"likelihood.neffectiveobs\"        #>  [41] \"likelihood.nobs\"                 \"likelihood.nparams\"              #>  [43] \"likelihood.ssqerr\"               \"log\"                             #>  [45] \"mhe\"                             \"mhe_b(?)\"                        #>  [47] \"mhe_f(?)\"                        \"omhe\"                            #>  [49] \"omhe_b(?)\"                       \"omhe_f(?)\"                       #>  [51] \"out\"                             \"out_b(?)\"                        #>  [53] \"out_f(?)\"                        \"out_i\"                           #>  [55] \"out_i_b(?)\"                      \"out_i_f(?)\"                      #>  [57] \"out_s\"                           \"out_s_b(?)\"                      #>  [59] \"out_s_f(?)\"                      \"out_t\"                           #>  [61] \"out_t_b(?)\"                      \"out_t_f(?)\"                      #>  [63] \"period\"                          \"reg_i\"                           #>  [65] \"reg_i_b(?)\"                      \"reg_i_f(?)\"                      #>  [67] \"reg_s\"                           \"reg_s_b(?)\"                      #>  [69] \"reg_s_f(?)\"                      \"reg_sa\"                          #>  [71] \"reg_sa_b(?)\"                     \"reg_sa_f(?)\"                     #>  [73] \"reg_t\"                           \"reg_t_b(?)\"                      #>  [75] \"reg_t_f(?)\"                      \"reg_u\"                           #>  [77] \"reg_u_b(?)\"                      \"reg_u_f(?)\"                      #>  [79] \"reg_y\"                           \"reg_y_b(?)\"                      #>  [81] \"reg_y_f(?)\"                      \"regression.description\"          #>  [83] \"regression.details.coefficients\" \"regression.details.covar\"        #>  [85] \"regression.details.covar-ml\"     \"regression.easter\"               #>  [87] \"regression.espan.end\"            \"regression.espan.missing\"        #>  [89] \"regression.espan.n\"              \"regression.espan.start\"          #>  [91] \"regression.leaster\"              \"regression.lp\"                   #>  [93] \"regression.mean\"                 \"regression.missing(*)\"           #>  [95] \"regression.ml.parameters\"        \"regression.ml.pcorr\"             #>  [97] \"regression.ml.pcovar\"            \"regression.ml.pcovar-ml\"         #>  [99] \"regression.ml.pscore\"            \"regression.mu\"                   #> [101] \"regression.nao\"                  \"regression.nlp\"                  #> [103] \"regression.nls\"                  \"regression.nmh\"                  #> [105] \"regression.nout\"                 \"regression.nso\"                  #> [107] \"regression.ntc\"                  \"regression.ntd\"                  #> [109] \"regression.nusers\"               \"regression.out(*)\"               #> [111] \"regression.outlier(*)\"           \"regression.td(*)\"                #> [113] \"regression.type\"                 \"regression.user(*)\"              #> [115] \"residuals.bp\"                    \"residuals.bp2\"                   #> [117] \"residuals.doornikhansen\"         \"residuals.kurtosis\"              #> [119] \"residuals.lb\"                    \"residuals.lb2\"                   #> [121] \"residuals.lruns\"                 \"residuals.ludruns\"               #> [123] \"residuals.mean\"                  \"residuals.nruns\"                 #> [125] \"residuals.nudruns\"               \"residuals.res\"                   #> [127] \"residuals.seasbp\"                \"residuals.seaslb\"                #> [129] \"residuals.ser\"                   \"residuals.skewness\"              #> [131] \"residuals.tsres\"                 \"residuals.type\"                  #> [133] \"span.end\"                        \"span.missing\"                    #> [135] \"span.n\"                          \"span.start\"                      #> [137] \"tde\"                             \"tde_b(?)\"                        #> [139] \"tde_f(?)\"                        \"y\"                               #> [141] \"y_b(?)\"                          \"y_eb(?)\"                         #> [143] \"y_ef(?)\"                         \"y_f(?)\"                          #> [145] \"yc\"                              \"ycal\"                            #> [147] \"ycal_f(?)\"                       y = rjd3toolkit::ABS$X0.2.09.10.M m <- tramoseats(y,\"rsafull\", userdefined=c(\"ycal\",\"variancedecomposition.seasonality\")) m$user_defined$ycal #>            Jan       Feb       Mar       Apr       May       Jun       Jul #> 1982                                443.8278  510.6464  441.5556  447.2441 #> 1983  385.2708  381.9008  463.5100  496.9624  524.2277  456.2137  471.7869 #> 1984  425.7682  401.8572  484.2305  490.2518  587.0210  494.0008  557.9031 #> 1985  506.9057  457.1696  543.2305  576.5562  692.6467  564.5811  606.3516 #> 1986  564.2652  483.1348  542.3265  606.3810  732.1905  570.9575  651.1328 #> 1987  598.0465  518.4960  631.6257  648.5345  719.3164  674.5692  713.0216 #> 1988  635.6984  540.2656  665.7617  706.5474  760.5717  712.3406  688.5893 #> 1989  648.3545  557.6964  693.4493  718.9404  769.3060  777.8438  726.5485 #> 1990  682.1958  592.2493  726.0960  748.6074  800.6617  821.7550  729.2576 #> 1991  681.3724  610.9402  706.5284  771.9465  819.1489  750.5975  799.1810 #> 1992  731.9128  635.8665  738.5989  799.8688  885.1420  831.0129  779.5861 #> 1993  767.7745  649.6355  777.8968  810.9673  921.1143  882.6137  751.5766 #> 1994  757.6383  671.2563  806.5525  821.0640  932.2342  900.3907  788.7294 #> 1995  772.4813  689.5431  822.8058  887.8325  932.6628  897.0479  894.6980 #> 1996  838.0084  705.3053  838.9181  878.2254  952.2779  913.0980  913.3497 #> 1997  831.7101  734.9064  849.9486  866.2586  969.1100  856.5394  953.4305 #> 1998  893.6114  723.5909  866.4191  933.3085  977.4240  854.5549 1030.9091 #> 1999  949.0141  746.0200  916.1835  935.0630 1027.3889  889.8768 1027.2683 #> 2000  939.7073  770.2115  914.8035 1004.1523 1058.4999 1152.9657  873.0570 #> 2001  977.6262  823.0063 1016.5144 1045.4950 1103.0575 1011.7933 1130.1799 #> 2002 1009.1970  850.0829 1025.5075 1109.4281 1177.8851 1148.6583 1082.0378 #> 2003 1087.2862  909.7928 1138.5197 1185.9589 1205.1528 1277.1743 1160.4931 #> 2004 1152.3525  979.4507 1167.1031 1231.9334 1300.7971 1377.1002 1272.1761 #> 2005 1266.2527 1048.0045 1245.3569 1168.0058 1297.9410 1434.7448 1299.7060 #> 2006 1225.5594 1048.4086 1221.7328 1321.5573 1275.0684 1413.6319 1434.6055 #> 2007 1274.7666 1057.8046 1330.3101 1310.4905 1340.2615 1472.4263 1501.3282 #> 2008 1372.3665 1108.6542 1339.1719 1424.9903 1384.4011 1446.0679 1553.5095 #> 2009 1413.5290 1067.5036 1460.2463 1451.8667 1530.7133 1509.6064 1553.5344 #> 2010 1480.0388 1123.3743 1399.7360 1363.2575 1450.0489 1543.5542 1540.1718 #> 2011 1435.2151 1129.0321 1330.6594 1417.5987 1446.5030 1452.2080 1543.9109 #> 2012 1398.7619 1097.1817 1347.8930 1399.6272 1430.3912 1595.7290 1461.3900 #> 2013 1371.9738 1125.0919 1372.6810 1381.6006 1426.1420 1545.0108 1398.7931 #> 2014 1435.1426 1075.8893 1347.4271 1399.8856 1375.3413 1507.9715 1474.2779 #> 2015 1433.2073 1064.6748 1427.4659 1399.2055 1439.3380 1489.8874 1524.0601 #> 2016 1530.5119 1133.0774 1389.6337 1468.9923 1488.3919 1574.7464 1479.2329 #> 2017 1466.6261 1103.6731 1389.9049 1492.0682 1460.9572 1540.4532 1468.6419 #>            Aug       Sep       Oct       Nov       Dec #> 1982  450.1007  458.8776  456.0708  526.0734  879.5812 #> 1983  460.8996  477.5088  493.0695  565.8239  938.8132 #> 1984  497.8985  525.3881  559.8794  616.4978 1056.0404 #> 1985  567.4574  580.9652  625.5077  688.1853 1219.1939 #> 1986  585.4268  665.9170  658.3259  704.3698 1375.9594 #> 1987  611.2314  668.9000  690.7880  766.7792 1433.5330 #> 1988  654.7350  715.5213  741.2806  865.8987 1529.4559 #> 1989  710.6302  748.2078  771.7626  911.4408 1581.0856 #> 1990  751.1008  744.7688  791.2345  921.4822 1588.1246 #> 1991  750.5050  765.0252  842.9775  932.6189 1643.0114 #> 1992  735.1845  815.6541  879.1936  999.3303 1620.8613 #> 1993  748.1487  833.3991  893.5038  975.8868 1691.8066 #> 1994  803.5063  877.4608  949.5626  995.3407 1770.5644 #> 1995  857.9991  845.8430  945.1704 1053.1197 1871.0286 #> 1996  865.9448  860.1504  916.7110 1067.7682 1886.6448 #> 1997  842.2248  944.7993  966.2238 1116.8014 1890.1712 #> 1998  883.8267  934.6492  994.9257 1173.7953 1939.7515 #> 1999  923.5073  988.8906 1051.4713 1214.5264 2046.9821 #> 2000  990.8373 1041.1135 1098.9686 1216.0109 2193.7135 #> 2001  952.4757 1017.9143 1138.6695 1330.3374 2305.8338 #> 2002 1038.3739 1093.7333 1189.3582 1475.9803 2401.0163 #> 2003 1109.3995 1205.4718 1301.7168 1535.7652 2438.4828 #> 2004 1197.9414 1300.2683 1368.4138 1546.0261 2473.4697 #> 2005 1217.8332 1264.0580 1335.4430 1532.6098 2470.5097 #> 2006 1216.6523 1203.5098 1380.4868 1601.5825 2630.5421 #> 2007 1301.1234 1359.9994 1449.0877 1669.7476 2790.9783 #> 2008 1277.8444 1391.6951 1436.5273 1684.1964 2903.1042 #> 2009 1362.9769 1392.8203 1494.6778 1791.6963 2744.2422 #> 2010 1359.3370 1402.9756 1475.8075 1715.8505 2722.3201 #> 2011 1301.1210 1331.2277 1427.1887 1651.6050 2659.9868 #> 2012 1344.1480 1374.5712 1451.0995 1654.9089 2797.3792 #> 2013 1302.6678 1402.5291 1436.6748 1647.5577 2798.2425 #> 2014 1263.5387 1364.6319 1462.5398 1696.0548 2773.1430 #> 2015 1353.2232 1393.0193 1525.8515 1785.4028 2860.5860 #> 2016 1301.5234 1378.5137 1521.3728 1675.7821 2776.7905 #> 2017 1279.3896                                         m$user_defined$variancedecomposition.seasonality #> [1] 0.9704556"}]
+[{"path":"https://rjdemetra.github.io/rjd3tramoseats/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jean Palate. Author, maintainer. Alain Quartier-la-Tente. Author.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palate J, Quartier-la-Tente (2023). rjd3tramoseats: Seasonal Adjustment TRAMO-SEATS 'JDemetra+ 3.0'. R package version 3.2.2, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/, https://github.com/rjdemetra/rjd3tramoseats.","code":"@Manual{,   title = {rjd3tramoseats: Seasonal Adjustment with TRAMO-SEATS and 'JDemetra+ 3.0'},   author = {Jean Palate and Alain Quartier-la-Tente},   year = {2023},   note = {R package version 3.2.2, https://tanguybarthelemy.github.io/rjd3tramoseats/, https://rjdemetra.github.io/rjd3tramoseats/},   url = {https://github.com/rjdemetra/rjd3tramoseats}, }"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"rjd3tramoseats","dir":"","previous_headings":"","what":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"rjd3tramoseats offers full acces options outputs TRAMO-SEATS (rjd3tramoseats::tramoseats()), including TRAMO modelling (rjd3tramoseats::tramo()) SEATS decomposition (rjd3tramoseats::seats_decompose()). specification can created functions rjd3tramoseats::tramo_spec() rjd3tramoseats::tramoseats_spec() can modified function: pre-processing: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(); decomposition: rjd3tramoseats::set_seats(); benchmarking: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"get current stable version (latest release): get current development version GitHub:","code":"# install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3toolkit@*release\") remotes::install_github(\"rjdemetra/rjd3tramoseats@*release\") # Install development version from GitHub # install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3tramoseats\")"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with TRAMO-SEATS and JDemetra+ 3.0","text":"get final components can use function rjd3toolkit::sa_decomposition():","code":"library(\"rjd3tramoseats\") y <- rjd3toolkit::ABS$X0.2.09.10.M ts_model <- tramoseats(y)  summary(ts_model$result$preprocessing) # Summary of tramo model #> Log-transformation: yes  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error  T-stat Pr(>|t|)     #> theta(1)  -0.82783    0.02571 -32.196  < 2e-16 *** #> btheta(1) -0.42554    0.06388  -6.661 9.01e-11 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Regression model: #>                   Estimate Std. Error T-stat Pr(>|t|)     #> monday          -0.0109446  0.0034805 -3.145 0.001788 **  #> tuesday          0.0048940  0.0035307  1.386 0.166479     #> wednesday        0.0001761  0.0034970  0.050 0.959867     #> thursday         0.0132928  0.0035330  3.763 0.000193 *** #> friday          -0.0024801  0.0035383 -0.701 0.483747     #> saturday         0.0153509  0.0035171  4.365 1.62e-05 *** #> lp               0.0410667  0.0101178  4.059 5.93e-05 *** #> easter           0.0503888  0.0072698  6.931 1.68e-11 *** #> AO (2000-06-01)  0.1681662  0.0299743  5.610 3.78e-08 *** #> AO (2000-07-01) -0.1972348  0.0298664 -6.604 1.28e-10 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> Number of observations:  425 , Number of effective observations:  412 , Number of parameters:  13  #> Loglikelihood:  781.358, Adjusted loglikelihood:  -2086.269 #> Standard error of the regression (ML estimate):  0.03615788  #> AIC:  4198.538 , AICc:  4199.452 , BIC:  4250.811 plot(ts_model) # Plot of the final decomposition rjd3toolkit::sa_decomposition(ts_model) #> Last values #>          series       sa    trend      seas       irr #> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979 #> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681 #> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508 #> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588 #> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587 #> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303 #> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913 #> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187 #> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924 #> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726 #> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781 #> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated-rjd3tramoseats","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"Deprecated functions","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"","code":"fast_tramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  fast_tramo(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )  spec_tramoseats(   name = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\") )  spec_tramo(name = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"))"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/deprecated-rjd3tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions — deprecated-rjd3tramoseats","text":"ts, spec, context, userdefined, name Parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":null,"dir":"Reference","previous_headings":"","what":"Java Utility Functions — jd3_utilities","title":"Java Utility Functions — jd3_utilities","text":"functions used JDemetra+ 3.0 packages easily interact R Java objects.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Java Utility Functions — jd3_utilities","text":"","code":".tramoseats_rslts(jrslts)  .jd2r_spec_tramo(jspec)  .r2jd_spec_tramo(spec)  .jd2r_spec_tramoseats(jspec)  .r2jd_spec_tramoseats(spec)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/jd3_utilities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Java Utility Functions — jd3_utilities","text":"spec, jspec, jrslts parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Refresh a specification with constraints — refresh","title":"Refresh a specification with constraints — refresh","text":"Function allowing create new specification updating specification used previous estimation. selected parameters kept fixed (previous estimation results) others freed re-estimation domain constraints. See details examples.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refresh a specification with constraints — refresh","text":"","code":"tramo_refresh(   spec,   refspec = NULL,   policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\",     \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\"),   period = 0,   start = NULL,   end = NULL )  tramoseats_refresh(   spec,   refspec = NULL,   policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\",     \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"),   period = 0,   start = NULL,   end = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refresh a specification with constraints — refresh","text":"spec current specification refreshed (\"result_spec\"). refspec reference specification used define domain considered re-estimation (\"domain_spec\"). default \"TRfull\" \"RSAfull\" specification. policy refresh policy apply (see details). period, start, end specify span outliers re-identified (.e.: re-detected) policy = \"Outliers\" policy = \"Outliers_StochasticComponent\". Span definition: period: numeric, number observations year (12, 4...). start end: first last date outliers re-identfied, defined arrays two elements: year first period (example, period = 12, c(1980, 1) January 1980). specified, outliers re-identified whole series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refresh a specification with constraints — refresh","text":"new specification, object class \"JD3_TRAMOSEATS_SPEC\" \"JD3_TRAMO_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refresh a specification with constraints — refresh","text":"selection constraints kept fixed re-estimated called revision policy. User-defined parameters always copied new refreshed specifications. Tramo (reg-arima pre-adjustment part) refreshed, final ARIMA model handed SEATS decomposition part might changed result refreshing process. Available refresh policies : Current: applying current pre-adjustment reg-arima model adding new raw data points Additive Outliers (defined new intervention variables) Fixed: applying current pre-adjustment reg-arima model replacing forecasts new raw data points. FixedParameters: pre-adjustment reg-arima model partially modified: regression coefficients re-estimated regression variables, Arima orders coefficients unchanged. FixedAutoRegressiveParameters: FixedParameters Arima Moving Average coefficients (MA) also re-estimated, Auto-regressive (AR) coefficients kept fixed. using Seats decomposition avoids possible re-allocation roots trend seasonal components, might led strong revisions. FreeParameters: regression Arima model coefficients re-estimated, regression variables Arima orders kept fixed. Outliers: regression variables Arima orders kept fixed, outliers re-detected defined span, thus regression Arima model coefficients re-estimated Outliers_StochasticComponent: \"Outliers\" Arima model orders (p,d,q)(P,D,Q) can also re-identified.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refresh a specification with constraints — refresh","text":"information revision policies JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/t-rev-policies-production","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/refresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Refresh a specification with constraints — refresh","text":"","code":"y<- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <-window(y,end = 2009) # raw series for second (refreshed) estimation y_new <-window(y,end = 2010) # specification for first estimation spec_ts_1<-tramoseats_spec(\"RSAFull\") # first estimation sa_ts<- tramoseats(y_raw, spec_ts_1) # refreshing the specification current_result_spec <- sa_ts$result_spec current_domain_spec <- sa_ts$estimation_spec spec_ts_ref <- tramoseats_refresh(current_result_spec, # point spec to be refreshed   current_domain_spec, #domain spec (set of constraints)   policy = \"FixedAutoRegressiveParameters\") # 2nd estimation with refreshed specification sa_ts_ref <- tramoseats(y_new, spec_ts_ref)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":null,"dir":"Reference","previous_headings":"","what":"SEATS Decomposition — seats_decompose","title":"SEATS Decomposition — seats_decompose","text":"SEATS Decomposition","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SEATS Decomposition — seats_decompose","text":"","code":"seats_decompose(   sarima,   seas.tolerance = 2,   trend.boundary = 0.5,   seas.boundary = 0.8,   seas.boundary.unique = 0.8,   approximation = c(\"None\", \"Legacy\", \"Noisy\") )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SEATS Decomposition — seats_decompose","text":"sarima SARIMA model (see rjd3toolkit::sarima_model()). seas.tolerance numeric: seasonal tolerance (epsphi). tolerance (measured degrees) allocate AR non-real roots seasonal component (modulus inverse complex AR root greater trend boundary frequency root differs one seasonal frequencies less Seasonal tolerance) transitory component (otherwise). Possible values [0,10]. Default value 2. trend.boundary numeric: trend boundary (rmod). boundary beyond AR root integrated trend component. modulus inverse real root greater trend boundary, AR root integrated trend component. value, root integrated transitory component. Possible values [0,1]. Default=0.5. seas.boundary numeric: seasonal boundary (sbound). boundary beyond real negative AR root integrated seasonal component. modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.boundary.unique numeric: seasonal boundary (unique),(sboundatpi). boundary beyond negative AR root integrated seasonal component, root unique seasonal root.  modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. approximation character: approximation mode. ARIMA model estimated TRAMO accept admissible decomposition, SEATS: \"None\" - performs approximation; \"Legacy\" - replaces model decomposable one; \"Noisy\" - estimates new model adding white noise non-admissible model estimated TRAMO. Default=\"Legacy\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/seats_decompose.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SEATS Decomposition — seats_decompose","text":"","code":"seats_decompose(rjd3toolkit::sarima_model(period = 12,phi = c(0,1),bd = 1)) #> model  #>  #> AR:  1 0 1  #> DIF:  1 0 0 0 0 0 0 0 0 0 0 0 -1  #> var:  1  #>  #> cmp-1  #>  #> DIF:  1 -1  #> MA:  1 1  #> var:  0.0004340278  #>  #> cmp-2  #>  #> AR:  1 0 1  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.5913351 0.2222453 0.0112871 -0.1344438 -0.2247946 -0.2715816 -0.2845599 -0.2706347 -0.2410118 -0.1721786 -0.1381599 -0.04996781 -0.03753463  #> var:  0.5974421  #>  #> cmp-3  #>  #> var:  0.0228588  #>"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Seats Specification — set_seats","title":"Set Seats Specification — set_seats","text":"Function allowing customize parameters decomposition part (Seats) Tramo-Seats seasonal adjustment process. (Seats Arima Model Based decomposition algorithm working conjunction Tramo.)","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Seats Specification — set_seats","text":"","code":"set_seats(   x,   approximation = c(NA, \"None\", \"Legacy\", \"Noisy\"),   trend.boundary = NA,   seas.boundary = NA,   seas.boundary.unique = NA,   seas.tolerance = NA,   ma.boundary = NA,   fcasts = NA,   bcasts = NA,   algorithm = c(NA, \"Burman\", \"KalmanSmoother\"),   bias = NA )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Seats Specification — set_seats","text":"x specification modified, object class \"JD3_TRAMOSEATS_SPEC\", generated tramoseats_spec() function approximation character: approximation mode. ARIMA model estimated TRAMO accept admissible decomposition, SEATS: \"None\" - performs approximation; \"Legacy\" - replaces model decomposable one; \"Noisy\" - estimates new model adding white noise non-admissible model estimated TRAMO. Default=\"Legacy\". trend.boundary numeric: trend boundary (rmod). boundary beyond AR root integrated trend component. modulus inverse real root greater trend boundary, AR root integrated trend component. value, root integrated transitory component. Possible values [0,1]. Default=0.5. seas.boundary numeric: seasonal boundary (sbound). boundary beyond real negative AR root integrated seasonal component. modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.boundary.unique numeric: seasonal boundary (unique),(sboundatpi). boundary beyond negative AR root integrated seasonal component, root unique seasonal root.  modulus inverse negative real root greater (equal) Seasonal boundary, AR root integrated seasonal component. Otherwise root integrated trend transitory component. Possible values [0,1]. Default=0.8. seas.tolerance numeric: seasonal tolerance (epsphi). tolerance (measured degrees) allocate AR non-real roots seasonal component (modulus inverse complex AR root greater trend boundary frequency root differs one seasonal frequencies less Seasonal tolerance) transitory component (otherwise). Possible values [0,10]. Default value 2. ma.boundary numeric: MA unit root boundary. modulus estimated MA root falls range [xl, 1], set xl. Possible values [0.9,1]. Default=0.95. bcasts, fcasts numeric: number backasts (bcasts) forecasts (fcasts) used decomposition periods (positive values) years (negative values).Default bcasts=0. Default fcasts=0. algorithm character: estimation method unobserved components. choice can made : \"Burman\": default value. May result significant underestimation components' standard deviation, may become numerically unstable roots MA polynomial near 1; \"KalmanSmoother\": disturbed (quasi-) unit roots MA; bias TODO.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Seats Specification — set_seats","text":"object class  \"JD3_TRAMOSEATS_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Set Seats Specification — set_seats","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/set_seats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set Seats Specification — set_seats","text":"","code":"init_spec<-tramoseats_spec(\"rsafull\") new_spec<- set_seats(init_spec,                     approximation = \"Legacy\",                     trend.boundary = 0.8,                     seas.boundary = 0.5,                     fcasts = -3,                     algorithm = \"KalmanSmoother\",                     bias = TRUE) y <- rjd3toolkit::ABS$X0.2.09.10.M sa<- tramoseats(y,spec=new_spec)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":null,"dir":"Reference","previous_headings":"","what":"TERROR Quality Control of Outliers — terror","title":"TERROR Quality Control of Outliers — terror","text":"TRAMO ERRORs (TERROR) controls quality data checking outliers end series","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TERROR Quality Control of Outliers — terror","text":"","code":"terror(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   nback = 1,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TERROR Quality Control of Outliers — terror","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. nback number last observations considered quality check. context dictionnary variables.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TERROR Quality Control of Outliers — terror","text":"mts object 7 variables: actual actual data end series. forecast forecast actual data end series. error absolute errors (= observed - forecasts). rel.error relative errors (\"scores\") : ratios forecast errors standard deviation forecasts last observations (positive values mean -estimation). raw transformed series. especially, chosen model implies log-transformation, values obtained log-transformation. transformations, leap year corrections length-periods corrections may also used. fraw forecast transformed series. efraw absolute errors transformed series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/terror.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TERROR Quality Control of Outliers — terror","text":"","code":"terror(rjd3toolkit::ABS$X0.2.09.10.M, nback = 2) #>          actual forecast     error rel. error transformed tr.fcast   tr.error #> Jul 2017 1445.5 1468.903 -23.40336 -0.4328439    7.276211 7.292271 0.03710535 #> Aug 2017 1303.1 1341.213 -38.11282 -0.7664734    7.172501 7.201330 0.03761155"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"TRAMO model, pre-adjustment TRAMO-SEATS","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"","code":"tramo(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )  tramo_fast(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   context = NULL,   userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context dictionnary variables. userdefined vector containing additional output variables (see tramoseats_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"tramo() function returns list results (\"JD3_regarima_rslts\" object), estimation specification result specification, tramo_fast() faster function returns results.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRAMO model, pre-adjustment in TRAMO-SEATS — tramo","text":"","code":"library(\"rjd3toolkit\") #>  #> Attaching package: ‘rjd3toolkit’ #> The following objects are masked from ‘package:stats’: #>  #>     aggregate, mad y = rjd3toolkit::ABS$X0.2.09.10.M sp = tramo_spec(\"trfull\") sp = add_outlier(sp,                  type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) tramo_fast(y, spec = sp) #> Log-transformation: yes  #> SARIMA model:  (2,1,2) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> phi(1)    -0.09251    0.39861 -0.232 #> phi(2)     0.12325    0.08546  1.442 #> theta(1)  -1.02295    0.40059 -2.554 #> theta(2)   0.24537    0.31899  0.769 #> btheta(1) -0.43575    0.06277 -6.942 #>  #> Regression model: #>                   Estimate Std. Error T-stat #> monday          -1.131e-02  3.604e-03 -3.137 #> tuesday          5.831e-03  3.623e-03  1.609 #> wednesday       -8.502e-05  3.605e-03 -0.024 #> thursday         1.293e-02  3.665e-03  3.529 #> friday          -2.076e-03  3.646e-03 -0.569 #> saturday         1.535e-02  3.635e-03  4.224 #> lp               3.881e-02  9.934e-03  3.907 #> easter           5.207e-02  7.566e-03  6.882 #> AO (2010-01-01)  3.674e-02  2.922e-02  1.258 #> AO (2015-01-01) -9.888e-03  2.935e-02 -0.337 #> AO (2000-06-01)  1.736e-01  2.928e-02  5.928 #> AO (2000-07-01) -1.831e-01  2.918e-02 -6.273 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  18  #>  #> Loglikelihood:  787.6949  #> Adjusted loglikelihood:  -2079.932  #>  #> Standard error of the regression (ML estimate):  0.03560084  #> AIC:  4195.864  #> AICC:  4197.604  #> BIC:  4268.242  #>  sp = set_transform(   set_tradingdays(     set_easter(sp, enabled = FALSE),     option = \"workingdays\"   ),   fun = \"None\" ) tramo_fast(y, spec = sp) #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>  sp = set_outlier(sp, outliers.type = c(\"AO\")) tramo_fast(y, spec = sp) #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecasts with TRAMO — tramo_forecast","title":"Forecasts with TRAMO — tramo_forecast","text":"Forecasts TRAMO","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecasts with TRAMO — tramo_forecast","text":"","code":"tramo_forecast(   ts,   spec = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"),   nf = -1,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecasts with TRAMO — tramo_forecast","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. nf forecasting horizon (numeric). forecast length periods (positive values) years (negative values). default, program generates one-year forecast (nf = -1). context dictionnary variables.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forecasts with TRAMO — tramo_forecast","text":"mts object 7 variables: forecast forecast actual data end series. error standard deviation forecast. fraw forecast transformed series. efraw standard deviation forecast transformed series.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_forecast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecasts with TRAMO — tramo_forecast","text":"","code":"tramo_forecast(rjd3toolkit::ABS$X0.2.09.10.M) #>          forecast     error     fraw      efraw #> Sep 2017 1394.524  51.63894 7.240309 0.03699180 #> Oct 2017 1465.852  55.09481 7.290192 0.03754581 #> Nov 2017 1719.851  65.39924 7.449993 0.03798498 #> Dec 2017 2766.983 107.04362 7.925513 0.03864273 #> Jan 2018 1460.928  57.22647 7.286827 0.03912637 #> Feb 2018 1090.905  43.08353 6.994763 0.03944732 #> Mar 2018 1447.182  58.38462 7.277374 0.04029456 #> Apr 2018 1395.393  57.07914 7.240932 0.04085424 #> May 2018 1482.589  60.88645 7.301545 0.04101588 #> Jun 2018 1556.902  64.92067 7.350453 0.04164443 #> Jul 2018 1433.030  60.29701 7.267546 0.04202090 #> Aug 2018 1302.717  55.47047 7.172207 0.04252290"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection with a Tramo Model — tramo_outliers","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"Tramo particular regarima model estimation algorithm, mainly used linearized series performing decomposition Seats","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"","code":"tramo_outliers(   y,   order = c(0L, 1L, 1L),   seasonal = c(0L, 1L, 1L),   mean = F,   X = NULL,   X.td = NULL,   ao = T,   ls = T,   tc = F,   so = F,   cv = 0,   ml = F,   clean = F )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"y dependent variable (ts object). order, seasonal orders ARIMA model. mean Boolean include mean. X user defined regressors (calendar). X.td calendar regressors. ao, ls, , tc Boolean indicate type outliers detected. cv numeric. entered critical value outliers' detection procedure. equal 0 critical value outliers' detection procedure automatically determined number observations. ml Use maximum likelihood (otherwise approximation means Hannan-Rissanen). clean Clean missing values beginning/end series. Regression variables automatically resized, need .","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"\"JD3_REGARIMA_OUTLIERS\" object.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramo_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection with a Tramo Model — tramo_outliers","text":"","code":"tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M) #> $model #> $model$y #>         Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct #> 1982                       460.1  502.6  443.8  459.1  438.4  465.1  452.7 #> 1983  379.2  378.0  472.1  503.4  510.6  462.4  468.3  458.2  482.7  485.3 #> 1984  414.7  414.5  484.7  487.3  597.9  500.4  543.4  503.4  522.8  556.6 #> 1985  516.3  452.5  525.8  587.7  700.3  561.8  602.8  582.5  563.1  637.1 #> 1986  570.5  478.2  547.4  594.3  751.6  553.4  663.2  581.1  661.9  665.6 #> 1987  613.9  513.2  599.9  674.1  714.0  670.5  720.9  601.6  672.3  709.1 #> 1988  631.0  551.1  678.1  715.7  740.8  722.0  683.5  650.9  723.3  729.6 #> 1989  631.5  552.0  719.0  697.6  764.8  786.3  715.1  723.8  757.9  751.7 #> 1990  678.2  586.2  726.8  744.1  815.5  832.4  710.3  759.4  741.1  786.6 #> 1991  694.0  604.7  719.2  748.2  828.2  746.9  794.5  770.4  741.5  858.6 #> 1992  740.0  665.9  701.5  831.4  878.6  826.0  788.2  723.6  819.8  902.5 #> 1993  762.1  643.0  754.1  840.7  906.6  887.1  771.5  728.7  844.7  886.9 #> 1994  745.7  664.4  821.5  831.7  908.0  912.6  782.9  798.8  887.0  934.6 #> 1995  752.4  682.5  811.2  906.0  927.2  906.8  880.6  873.9  856.8  920.6 #> 1996  833.1  737.1  812.0  895.2  962.8  908.6  908.0  888.9  833.7  933.7 #> 1997  840.9  727.4  857.9  849.0  994.8  830.2  971.1  836.0  939.1  976.9 #> 1998  917.3  716.2  822.9  970.1  970.2  849.4 1042.3  869.9  939.4 1021.3 #> 1999  942.0  738.4  903.2  953.2 1011.2  894.4 1054.5  899.5 1002.3 1043.7 #> 2000  924.9  798.2  901.9 1024.7 1052.3 1165.5  859.3 1009.2 1054.6 1070.4 #> 2001  971.9  814.6 1017.5 1039.2 1123.5 1024.9 1100.8  963.0 1012.9 1132.0 #> 2002 1027.9  841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4 #> 2003 1099.3  900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1 #> 2004 1182.9  989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3 #> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4 #> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6 #> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6 #> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4 #> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3 #> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9 #> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7 #> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6 #> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3 #> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7 #> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3 #> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4 #> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1               #>         Nov    Dec #> 1982  522.9  889.3 #> 1983  568.7  963.7 #> 1984  623.2 1039.4 #> 1985  697.1 1187.5 #> 1986  700.9 1367.9 #> 1987  743.2 1460.1 #> 1988  870.3 1570.0 #> 1989  923.8 1569.4 #> 1990  931.5 1563.1 #> 1991  944.7 1600.3 #> 1992  968.6 1650.9 #> 1993  970.0 1710.5 #> 1994 1000.4 1817.5 #> 1995 1067.4 1857.2 #> 1996 1081.6 1837.6 #> 1997 1111.3 1879.1 #> 1998 1137.7 1975.7 #> 1999 1207.2 2069.6 #> 2000 1232.5 2177.5 #> 2001 1344.8 2269.5 #> 2002 1495.1 2338.6 #> 2003 1528.2 2424.2 #> 2004 1536.7 2500.8 #> 2005 1540.4 2536.0 #> 2006 1623.3 2611.1 #> 2007 1687.9 2747.0 #> 2008 1675.9 2886.1 #> 2009 1736.6 2795.1 #> 2010 1705.5 2752.4 #> 2011 1660.0 2730.5 #> 2012 1672.9 2753.3 #> 2013 1668.9 2725.5 #> 2014 1687.7 2756.9 #> 2015 1730.5 2913.6 #> 2016 1684.3 2850.4 #> 2017               #>  #> $model$variables #> [1] \"AO.220\" \"AO.219\" #>  #> $model$X #>        [,1] [,2] #>   [1,]    0    0 #>   [2,]    0    0 #>   [3,]    0    0 #>   [4,]    0    0 #>   [5,]    0    0 #>   [6,]    0    0 #>   [7,]    0    0 #>   [8,]    0    0 #>   [9,]    0    0 #>  [10,]    0    0 #>  [11,]    0    0 #>  [12,]    0    0 #>  [13,]    0    0 #>  [14,]    0    0 #>  [15,]    0    0 #>  [16,]    0    0 #>  [17,]    0    0 #>  [18,]    0    0 #>  [19,]    0    0 #>  [20,]    0    0 #>  [21,]    0    0 #>  [22,]    0    0 #>  [23,]    0    0 #>  [24,]    0    0 #>  [25,]    0    0 #>  [26,]    0    0 #>  [27,]    0    0 #>  [28,]    0    0 #>  [29,]    0    0 #>  [30,]    0    0 #>  [31,]    0    0 #>  [32,]    0    0 #>  [33,]    0    0 #>  [34,]    0    0 #>  [35,]    0    0 #>  [36,]    0    0 #>  [37,]    0    0 #>  [38,]    0    0 #>  [39,]    0    0 #>  [40,]    0    0 #>  [41,]    0    0 #>  [42,]    0    0 #>  [43,]    0    0 #>  [44,]    0    0 #>  [45,]    0    0 #>  [46,]    0    0 #>  [47,]    0    0 #>  [48,]    0    0 #>  [49,]    0    0 #>  [50,]    0    0 #>  [51,]    0    0 #>  [52,]    0    0 #>  [53,]    0    0 #>  [54,]    0    0 #>  [55,]    0    0 #>  [56,]    0    0 #>  [57,]    0    0 #>  [58,]    0    0 #>  [59,]    0    0 #>  [60,]    0    0 #>  [61,]    0    0 #>  [62,]    0    0 #>  [63,]    0    0 #>  [64,]    0    0 #>  [65,]    0    0 #>  [66,]    0    0 #>  [67,]    0    0 #>  [68,]    0    0 #>  [69,]    0    0 #>  [70,]    0    0 #>  [71,]    0    0 #>  [72,]    0    0 #>  [73,]    0    0 #>  [74,]    0    0 #>  [75,]    0    0 #>  [76,]    0    0 #>  [77,]    0    0 #>  [78,]    0    0 #>  [79,]    0    0 #>  [80,]    0    0 #>  [81,]    0    0 #>  [82,]    0    0 #>  [83,]    0    0 #>  [84,]    0    0 #>  [85,]    0    0 #>  [86,]    0    0 #>  [87,]    0    0 #>  [88,]    0    0 #>  [89,]    0    0 #>  [90,]    0    0 #>  [91,]    0    0 #>  [92,]    0    0 #>  [93,]    0    0 #>  [94,]    0    0 #>  [95,]    0    0 #>  [96,]    0    0 #>  [97,]    0    0 #>  [98,]    0    0 #>  [99,]    0    0 #> [100,]    0    0 #> [101,]    0    0 #> [102,]    0    0 #> [103,]    0    0 #> [104,]    0    0 #> [105,]    0    0 #> [106,]    0    0 #> [107,]    0    0 #> [108,]    0    0 #> [109,]    0    0 #> [110,]    0    0 #> [111,]    0    0 #> [112,]    0    0 #> [113,]    0    0 #> [114,]    0    0 #> [115,]    0    0 #> [116,]    0    0 #> [117,]    0    0 #> [118,]    0    0 #> [119,]    0    0 #> [120,]    0    0 #> [121,]    0    0 #> [122,]    0    0 #> [123,]    0    0 #> [124,]    0    0 #> [125,]    0    0 #> [126,]    0    0 #> [127,]    0    0 #> [128,]    0    0 #> [129,]    0    0 #> [130,]    0    0 #> [131,]    0    0 #> [132,]    0    0 #> [133,]    0    0 #> [134,]    0    0 #> [135,]    0    0 #> [136,]    0    0 #> [137,]    0    0 #> [138,]    0    0 #> [139,]    0    0 #> [140,]    0    0 #> [141,]    0    0 #> [142,]    0    0 #> [143,]    0    0 #> [144,]    0    0 #> [145,]    0    0 #> [146,]    0    0 #> [147,]    0    0 #> [148,]    0    0 #> [149,]    0    0 #> [150,]    0    0 #> [151,]    0    0 #> [152,]    0    0 #> [153,]    0    0 #> [154,]    0    0 #> [155,]    0    0 #> [156,]    0    0 #> [157,]    0    0 #> [158,]    0    0 #> [159,]    0    0 #> [160,]    0    0 #> [161,]    0    0 #> [162,]    0    0 #> [163,]    0    0 #> [164,]    0    0 #> [165,]    0    0 #> [166,]    0    0 #> [167,]    0    0 #> [168,]    0    0 #> [169,]    0    0 #> [170,]    0    0 #> [171,]    0    0 #> [172,]    0    0 #> [173,]    0    0 #> [174,]    0    0 #> [175,]    0    0 #> [176,]    0    0 #> [177,]    0    0 #> [178,]    0    0 #> [179,]    0    0 #> [180,]    0    0 #> [181,]    0    0 #> [182,]    0    0 #> [183,]    0    0 #> [184,]    0    0 #> [185,]    0    0 #> [186,]    0    0 #> [187,]    0    0 #> [188,]    0    0 #> [189,]    0    0 #> [190,]    0    0 #> [191,]    0    0 #> [192,]    0    0 #> [193,]    0    0 #> [194,]    0    0 #> [195,]    0    0 #> [196,]    0    0 #> [197,]    0    0 #> [198,]    0    0 #> [199,]    0    0 #> [200,]    0    0 #> [201,]    0    0 #> [202,]    0    0 #> [203,]    0    0 #> [204,]    0    0 #> [205,]    0    0 #> [206,]    0    0 #> [207,]    0    0 #> [208,]    0    0 #> [209,]    0    0 #> [210,]    0    0 #> [211,]    0    0 #> [212,]    0    0 #> [213,]    0    0 #> [214,]    0    0 #> [215,]    0    0 #> [216,]    0    0 #> [217,]    0    0 #> [218,]    0    0 #> [219,]    0    1 #> [220,]    1    0 #> [221,]    0    0 #> [222,]    0    0 #> [223,]    0    0 #> [224,]    0    0 #> [225,]    0    0 #> [226,]    0    0 #> [227,]    0    0 #> [228,]    0    0 #> [229,]    0    0 #> [230,]    0    0 #> [231,]    0    0 #> [232,]    0    0 #> [233,]    0    0 #> [234,]    0    0 #> [235,]    0    0 #> [236,]    0    0 #> [237,]    0    0 #> [238,]    0    0 #> [239,]    0    0 #> [240,]    0    0 #> [241,]    0    0 #> [242,]    0    0 #> [243,]    0    0 #> [244,]    0    0 #> [245,]    0    0 #> [246,]    0    0 #> [247,]    0    0 #> [248,]    0    0 #> [249,]    0    0 #> [250,]    0    0 #> [251,]    0    0 #> [252,]    0    0 #> [253,]    0    0 #> [254,]    0    0 #> [255,]    0    0 #> [256,]    0    0 #> [257,]    0    0 #> [258,]    0    0 #> [259,]    0    0 #> [260,]    0    0 #> [261,]    0    0 #> [262,]    0    0 #> [263,]    0    0 #> [264,]    0    0 #> [265,]    0    0 #> [266,]    0    0 #> [267,]    0    0 #> [268,]    0    0 #> [269,]    0    0 #> [270,]    0    0 #> [271,]    0    0 #> [272,]    0    0 #> [273,]    0    0 #> [274,]    0    0 #> [275,]    0    0 #> [276,]    0    0 #> [277,]    0    0 #> [278,]    0    0 #> [279,]    0    0 #> [280,]    0    0 #> [281,]    0    0 #> [282,]    0    0 #> [283,]    0    0 #> [284,]    0    0 #> [285,]    0    0 #> [286,]    0    0 #> [287,]    0    0 #> [288,]    0    0 #> [289,]    0    0 #> [290,]    0    0 #> [291,]    0    0 #> [292,]    0    0 #> [293,]    0    0 #> [294,]    0    0 #> [295,]    0    0 #> [296,]    0    0 #> [297,]    0    0 #> [298,]    0    0 #> [299,]    0    0 #> [300,]    0    0 #> [301,]    0    0 #> [302,]    0    0 #> [303,]    0    0 #> [304,]    0    0 #> [305,]    0    0 #> [306,]    0    0 #> [307,]    0    0 #> [308,]    0    0 #> [309,]    0    0 #> [310,]    0    0 #> [311,]    0    0 #> [312,]    0    0 #> [313,]    0    0 #> [314,]    0    0 #> [315,]    0    0 #> [316,]    0    0 #> [317,]    0    0 #> [318,]    0    0 #> [319,]    0    0 #> [320,]    0    0 #> [321,]    0    0 #> [322,]    0    0 #> [323,]    0    0 #> [324,]    0    0 #> [325,]    0    0 #> [326,]    0    0 #> [327,]    0    0 #> [328,]    0    0 #> [329,]    0    0 #> [330,]    0    0 #> [331,]    0    0 #> [332,]    0    0 #> [333,]    0    0 #> [334,]    0    0 #> [335,]    0    0 #> [336,]    0    0 #> [337,]    0    0 #> [338,]    0    0 #> [339,]    0    0 #> [340,]    0    0 #> [341,]    0    0 #> [342,]    0    0 #> [343,]    0    0 #> [344,]    0    0 #> [345,]    0    0 #> [346,]    0    0 #> [347,]    0    0 #> [348,]    0    0 #> [349,]    0    0 #> [350,]    0    0 #> [351,]    0    0 #> [352,]    0    0 #> [353,]    0    0 #> [354,]    0    0 #> [355,]    0    0 #> [356,]    0    0 #> [357,]    0    0 #> [358,]    0    0 #> [359,]    0    0 #> [360,]    0    0 #> [361,]    0    0 #> [362,]    0    0 #> [363,]    0    0 #> [364,]    0    0 #> [365,]    0    0 #> [366,]    0    0 #> [367,]    0    0 #> [368,]    0    0 #> [369,]    0    0 #> [370,]    0    0 #> [371,]    0    0 #> [372,]    0    0 #> [373,]    0    0 #> [374,]    0    0 #> [375,]    0    0 #> [376,]    0    0 #> [377,]    0    0 #> [378,]    0    0 #> [379,]    0    0 #> [380,]    0    0 #> [381,]    0    0 #> [382,]    0    0 #> [383,]    0    0 #> [384,]    0    0 #> [385,]    0    0 #> [386,]    0    0 #> [387,]    0    0 #> [388,]    0    0 #> [389,]    0    0 #> [390,]    0    0 #> [391,]    0    0 #> [392,]    0    0 #> [393,]    0    0 #> [394,]    0    0 #> [395,]    0    0 #> [396,]    0    0 #> [397,]    0    0 #> [398,]    0    0 #> [399,]    0    0 #> [400,]    0    0 #> [401,]    0    0 #> [402,]    0    0 #> [403,]    0    0 #> [404,]    0    0 #> [405,]    0    0 #> [406,]    0    0 #> [407,]    0    0 #> [408,]    0    0 #> [409,]    0    0 #> [410,]    0    0 #> [411,]    0    0 #> [412,]    0    0 #> [413,]    0    0 #> [414,]    0    0 #> [415,]    0    0 #> [416,]    0    0 #> [417,]    0    0 #> [418,]    0    0 #> [419,]    0    0 #> [420,]    0    0 #> [421,]    0    0 #> [422,]    0    0 #> [423,]    0    0 #> [424,]    0    0 #> [425,]    0    0 #>  #> $model$b #> [1] -211.0035  189.6218 #>  #> $model$bcov #>           [,1]      [,2] #> [1,] 1516.9191  124.4537 #> [2,]  124.4537 1516.9191 #>  #> $model$linearized #>   [1]  460.1000  502.6000  443.8000  459.1000  438.4000  465.1000  452.7000 #>   [8]  522.9000  889.3000  379.2000  378.0000  472.1000  503.4000  510.6000 #>  [15]  462.4000  468.3000  458.2000  482.7000  485.3000  568.7000  963.7000 #>  [22]  414.7000  414.5000  484.7000  487.3000  597.9000  500.4000  543.4000 #>  [29]  503.4000  522.8000  556.6000  623.2000 1039.4000  516.3000  452.5000 #>  [36]  525.8000  587.7000  700.3000  561.8000  602.8000  582.5000  563.1000 #>  [43]  637.1000  697.1000 1187.5000  570.5000  478.2000  547.4000  594.3000 #>  [50]  751.6000  553.4000  663.2000  581.1000  661.9000  665.6000  700.9000 #>  [57] 1367.9000  613.9000  513.2000  599.9000  674.1000  714.0000  670.5000 #>  [64]  720.9000  601.6000  672.3000  709.1000  743.2000 1460.1000  631.0000 #>  [71]  551.1000  678.1000  715.7000  740.8000  722.0000  683.5000  650.9000 #>  [78]  723.3000  729.6000  870.3000 1570.0000  631.5000  552.0000  719.0000 #>  [85]  697.6000  764.8000  786.3000  715.1000  723.8000  757.9000  751.7000 #>  [92]  923.8000 1569.4000  678.2000  586.2000  726.8000  744.1000  815.5000 #>  [99]  832.4000  710.3000  759.4000  741.1000  786.6000  931.5000 1563.1000 #> [106]  694.0000  604.7000  719.2000  748.2000  828.2000  746.9000  794.5000 #> [113]  770.4000  741.5000  858.6000  944.7000 1600.3000  740.0000  665.9000 #> [120]  701.5000  831.4000  878.6000  826.0000  788.2000  723.6000  819.8000 #> [127]  902.5000  968.6000 1650.9000  762.1000  643.0000  754.1000  840.7000 #> [134]  906.6000  887.1000  771.5000  728.7000  844.7000  886.9000  970.0000 #> [141] 1710.5000  745.7000  664.4000  821.5000  831.7000  908.0000  912.6000 #> [148]  782.9000  798.8000  887.0000  934.6000 1000.4000 1817.5000  752.4000 #> [155]  682.5000  811.2000  906.0000  927.2000  906.8000  880.6000  873.9000 #> [162]  856.8000  920.6000 1067.4000 1857.2000  833.1000  737.1000  812.0000 #> [169]  895.2000  962.8000  908.6000  908.0000  888.9000  833.7000  933.7000 #> [176] 1081.6000 1837.6000  840.9000  727.4000  857.9000  849.0000  994.8000 #> [183]  830.2000  971.1000  836.0000  939.1000  976.9000 1111.3000 1879.1000 #> [190]  917.3000  716.2000  822.9000  970.1000  970.2000  849.4000 1042.3000 #> [197]  869.9000  939.4000 1021.3000 1137.7000 1975.7000  942.0000  738.4000 #> [204]  903.2000  953.2000 1011.2000  894.4000 1054.5000  899.5000 1002.3000 #> [211] 1043.7000 1207.2000 2069.6000  924.9000  798.2000  901.9000 1024.7000 #> [218] 1052.3000  975.8782 1070.3035 1009.2000 1054.6000 1070.4000 1232.5000 #> [225] 2177.5000  971.9000  814.6000 1017.5000 1039.2000 1123.5000 1024.9000 #> [232] 1100.8000  963.0000 1012.9000 1132.0000 1344.8000 2269.5000 1027.9000 #> [239]  841.4000 1043.9000 1075.3000 1190.9000 1143.0000 1075.7000 1065.9000 #> [246] 1060.1000 1211.4000 1495.1000 2338.6000 1099.3000  900.5000 1092.7000 #> [253] 1222.4000 1237.1000 1237.9000 1182.0000 1101.2000 1198.2000 1316.1000 #> [260] 1528.2000 2424.2000 1182.9000  989.8000 1131.4000 1277.1000 1280.3000 #> [267] 1384.1000 1305.9000 1166.8000 1317.9000 1358.3000 1536.7000 2500.8000 #> [274] 1246.3000 1037.3000 1300.8000 1153.7000 1264.2000 1454.2000 1290.1000 #> [281] 1210.7000 1277.8000 1314.4000 1540.4000 2536.0000 1193.7000 1037.7000 #> [288] 1204.5000 1348.6000 1267.6000 1429.0000 1412.0000 1239.2000 1219.1000 #> [295] 1344.6000 1623.3000 2611.1000 1267.3000 1047.0000 1331.6000 1302.6000 #> [302] 1365.1000 1491.5000 1462.3000 1315.5000 1353.3000 1440.6000 1687.9000 #> [309] 2747.0000 1397.8000 1140.5000 1351.7000 1396.6000 1421.1000 1401.6000 #> [316] 1582.3000 1268.4000 1383.3000 1452.4000 1675.9000 2886.1000 1451.0000 #> [323] 1056.6000 1386.9000 1509.1000 1519.4000 1500.5000 1570.7000 1341.5000 #> [330] 1399.9000 1534.3000 1736.6000 2795.1000 1469.1000 1111.9000 1379.9000 #> [337] 1389.7000 1427.2000 1551.4000 1581.0000 1324.0000 1422.0000 1464.9000 #> [344] 1705.5000 2752.4000 1412.6000 1117.5000 1321.6000 1472.6000 1408.9000 #> [351] 1471.9000 1532.5000 1293.5000 1345.7000 1404.7000 1660.0000 2730.5000 #> [358] 1362.4000 1131.7000 1349.2000 1391.2000 1456.9000 1616.4000 1423.4000 #> [365] 1359.0000 1367.8000 1442.6000 1672.9000 2753.3000 1397.4000 1113.6000 #> [372] 1397.3000 1339.1000 1441.9000 1537.4000 1390.6000 1337.2000 1359.4000 #> [379] 1463.3000 1668.9000 2725.5000 1451.0000 1064.9000 1293.2000 1442.9000 #> [386] 1411.8000 1461.6000 1501.6000 1254.2000 1356.4000 1478.7000 1687.7000 #> [393] 2756.9000 1471.2000 1053.8000 1367.2000 1442.2000 1428.7000 1480.9000 #> [400] 1540.9000 1331.9000 1400.1000 1566.3000 1730.5000 2913.6000 1519.2000 #> [407] 1155.8000 1451.5000 1451.0000 1449.7000 1596.1000 1468.3000 1293.9000 #> [414] 1393.5000 1497.4000 1684.3000 2850.4000 1428.5000 1092.4000 1370.3000 #> [421] 1522.6000 1452.4000 1557.2000 1445.5000 1303.1000 #>  #>  #> $likelihood #> $likelihood$initial #> $likelihood$initial$ll #> [1] -2218.964 #>  #> $likelihood$initial$ssq #> [1] 1139531 #>  #> $likelihood$initial$nobs #> [1] 425 #>  #> $likelihood$initial$neffective #> [1] -1 #>  #> $likelihood$initial$nparams #> [1] 3 #>  #> $likelihood$initial$df #> [1] 409 #>  #> $likelihood$initial$aic #> [1] 4443.928 #>  #> $likelihood$initial$aicc #> [1] 4443.987 #>  #> $likelihood$initial$bic #> [1] 4455.991 #>  #> $likelihood$initial$bic2 #> [1] 10.81551 #>  #> $likelihood$initial$bicc #> [1] 7.954332 #>  #> $likelihood$initial$hannanquinn #> [1] 4448.7 #>  #>  #> $likelihood$final #> $likelihood$final$ll #> [1] -2194.499 #>  #> $likelihood$final$ssq #> [1] 1014171 #>  #> $likelihood$final$nobs #> [1] 425 #>  #> $likelihood$final$neffective #> [1] -1 #>  #> $likelihood$final$nparams #> [1] 3 #>  #> $likelihood$final$df #> [1] 409 #>  #> $likelihood$final$aic #> [1] 4394.998 #>  #> $likelihood$final$aicc #> [1] 4395.057 #>  #> $likelihood$final$bic #> [1] 4407.061 #>  #> $likelihood$final$bic2 #> [1] 10.69675 #>  #> $likelihood$final$bicc #> [1] 7.837787 #>  #> $likelihood$final$hannanquinn #> [1] 4399.77 #>  #>  #>  #> attr(,\"class\") #> [1] \"JD3_REGARIMA_OUTLIERS\""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"Seasonal Adjustment  TRAMO-SEATS","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"","code":"tramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  tramoseats_fast(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )  jtramoseats(   ts,   spec = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\"),   context = NULL,   userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context dictionnary variables. userdefined vector containing additional output variables (see tramoseats_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"tramoseats() function returns list results, estimation specification result specification, tramoseats_fast() faster function returns results. jtramoseats() functions results java object custom outputs packages (use rjd3toolkit::dictionary() get list variables rjd3toolkit::result() get specific variable).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seasonal Adjustment with  TRAMO-SEATS — tramoseats","text":"","code":"library(\"rjd3toolkit\") sp = tramoseats_spec(\"rsafull\") y = rjd3toolkit::ABS$X0.2.09.10.M tramoseats_fast(y, spec = sp) #> TRAMO #> Log-transformation: yes  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error  T-stat #> theta(1)  -0.82783    0.02571 -32.196 #> btheta(1) -0.42554    0.06388  -6.661 #>  #> Regression model: #>                   Estimate Std. Error T-stat #> monday          -0.0109446  0.0034805 -3.145 #> tuesday          0.0048940  0.0035307  1.386 #> wednesday        0.0001761  0.0034970  0.050 #> thursday         0.0132928  0.0035330  3.763 #> friday          -0.0024801  0.0035383 -0.701 #> saturday         0.0153509  0.0035171  4.365 #> lp               0.0410667  0.0101178  4.059 #> easter           0.0503888  0.0072698  6.931 #> AO (2000-06-01)  0.1681662  0.0299743  5.610 #> AO (2000-07-01) -0.1972348  0.0298664 -6.604 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  13  #>  #> Loglikelihood:  781.358  #> Adjusted loglikelihood:  -2086.269  #>  #> Standard error of the regression (ML estimate):  0.03615788  #> AIC:  4198.538  #> AICC:  4199.452  #> BIC:  4250.811  #>  #>  #> Decomposition #> model  #>  #> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1  #> MA:  1 -0.8278316 0 0 0 0 0 0 0 0 0 0 -0.4255409 0.3522762  #> var:  1  #>  #> trend  #>  #> DIF:  1 -2 1  #> MA:  1 0.06437327 -0.9356267  #> var:  0.004098184  #>  #> seasonal  #>  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.6197472 0.3121522 0.07325877 -0.1026014 -0.2222985 -0.2934508 -0.3240296 -0.3220203 -0.295141 -0.2506158 -0.1950007  #> var:  0.1460322  #>  #> irregular  #>  #> var:  0.384587  #>  #>  #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #>  #>            Component #>  cycle         0.250 #>  seasonal     97.046 #>  irregular     0.600 #>  calendar      0.741 #>  others        0.325 #>  total        98.962 #>  #> Residual seasonality tests #>                 P.value #>  seas.ftest.i         1 #>  seas.ftest.sa        1 #>  seas.qstest.i        1 #>  seas.qstest.sa       1 #>  td.ftest.i           1 #>  td.ftest.sa          1 #>  #>  #> Final #> Last values #>          series       sa    trend      seas       irr #> Sep 2016 1393.5 1552.616 1561.206 0.8975174 0.9944979 #> Oct 2016 1497.4 1568.366 1559.217 0.9547514 1.0058681 #> Nov 2016 1684.3 1528.962 1557.382 1.1015974 0.9817508 #> Dec 2016 2850.4 1542.997 1556.132 1.8473143 0.9915588 #> Jan 2017 1428.5 1545.950 1555.502 0.9240275 0.9938587 #> Feb 2017 1092.4 1551.369 1555.210 0.7041521 0.9975303 #> Mar 2017 1370.3 1553.207 1555.087 0.8822391 0.9987913 #> Apr 2017 1522.6 1580.752 1554.759 0.9632123 1.0167187 #> May 2017 1452.4 1554.517 1553.908 0.9343093 1.0003924 #> Jun 2017 1557.2 1551.804 1552.778 1.0034774 0.9993726 #> Jul 2017 1445.5 1544.701 1551.717 0.9357801 0.9954781 #> Aug 2017 1303.1 1535.588 1550.949 0.8485999 0.9900960 sp = add_outlier(sp,                  type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) sp = set_transform(   set_tradingdays(     set_easter(sp, enabled = FALSE),     option = \"workingdays\"   ),   fun = \"None\" ) tramoseats_fast(y, spec = sp) #> TRAMO #> Log-transformation: no  #> SARIMA model:  (0,1,1) (0,1,1) #>  #> Coefficients #>           Estimate Std. Error T-stat #> theta(1)  -0.82355    0.02896 -28.43 #> btheta(1) -0.26081    0.04903  -5.32 #>  #> Regression model: #>                  Estimate Std. Error T-stat #> monday           -11.7873     3.8750 -3.042 #> tuesday            0.2507     3.8786  0.065 #> wednesday          3.0039     3.8746  0.775 #> thursday          12.8309     3.9423  3.255 #> friday            -5.4519     3.9132 -1.393 #> saturday          17.2998     3.9274  4.405 #> lp                33.6083    11.2479  2.988 #> AO (2010-01-01)   40.5331    33.7485  1.201 #> AO (2015-01-01)  -10.7096    33.7815 -0.317 #> AO (2000-06-01)  192.7411    33.9640  5.675 #> AO (2000-07-01) -200.7316    33.7986 -5.939 #> AO (2005-04-01) -177.1356    33.7257 -5.252 #> Number of observations:  425  #> Number of effective observations:  412  #> Number of parameters:  15  #>  #> Loglikelihood:  -2139.74  #> Standard error of the regression (ML estimate):  43.47307  #> AIC:  4309.481  #> AICC:  4310.693  #> BIC:  4369.796  #>  #>  #> Decomposition #> model  #>  #> DIF:  1 -1 0 0 0 0 0 0 0 0 0 0 -1 1  #> MA:  1 -0.8235461 0 0 0 0 0 0 0 0 0 0 -0.2608057 0.2147855  #> var:  1  #>  #> trend  #>  #> DIF:  1 -2 1  #> MA:  1 0.09197092 -0.9080291  #> var:  0.003491855  #>  #> seasonal  #>  #> DIF:  1 1 1 1 1 1 1 1 1 1 1 1  #> MA:  1 0.6220678 0.315157 0.07593327 -0.1008043 -0.2215881 -0.2938052 -0.3252816 -0.3239249 -0.2974269 -0.2530226 -0.1973046  #> var:  0.240472  #>  #> irregular  #>  #> var:  0.2654024  #>  #>  #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #>  #>            Component #>  cycle         0.198 #>  seasonal     98.409 #>  irregular     0.223 #>  calendar      0.246 #>  others        0.257 #>  total        99.333 #>  #> Residual seasonality tests #>                 P.value #>  seas.ftest.i         1 #>  seas.ftest.sa        1 #>  seas.qstest.i        1 #>  seas.qstest.sa       1 #>  td.ftest.i           1 #>  td.ftest.sa          1 #>  #>  #> Final #> Last values #>          series       sa    trend        seas          irr #> Sep 2016 1393.5 1557.498 1559.704 -163.997649  -2.20658744 #> Oct 2016 1497.4 1553.718 1557.296  -56.318379  -3.57749836 #> Nov 2016 1684.3 1538.837 1555.164  145.462939 -16.32645755 #> Dec 2016 2850.4 1544.533 1553.556 1305.866600  -9.02221292 #> Jan 2017 1428.5 1540.203 1552.499 -111.703036 -12.29569819 #> Feb 2017 1092.4 1547.827 1551.890 -455.426856  -4.06311664 #> Mar 2017 1370.3 1535.240 1551.720 -164.939565 -16.48081320 #> Apr 2017 1522.6 1581.396 1551.632  -58.795692  29.76367672 #> May 2017 1452.4 1552.527 1551.001 -100.127238   1.52621793 #> Jun 2017 1557.2 1549.996 1549.968    7.204385   0.02775996 #> Jul 2017 1445.5 1543.239 1548.979  -97.739206  -5.73936138 #> Aug 2017 1303.1 1544.526 1548.167 -241.425886  -3.64143843"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO-SEATS Dictionary — tramoseats_dictionary","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"TRAMO-SEATS Dictionary","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"","code":"tramoseats_dictionary()"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO-SEATS Dictionary — tramoseats_dictionary","text":"vector containing names available output objects (series, diagnostics, parameters).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":null,"dir":"Reference","previous_headings":"","what":"Revisions History — tramoseats_revisions","title":"Revisions History — tramoseats_revisions","text":"Compute revisions history","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Revisions History — tramoseats_revisions","text":"","code":"tramoseats_revisions(   ts,   spec,   data_ids = NULL,   ts_ids = NULL,   cmp_ids = NULL,   context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Revisions History — tramoseats_revisions","text":"ts time series used estimation. spec specification used. data_ids list list specify statistics export. sub-list must contain two elements: start (first date compute history, format \"YYYY-MM-DD\") id (name statistics, see tramoseats_dictionary()). See example. ts_ids list list specify specific date component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), period (date studied) id (name component, see tramoseats_dictionary()). See example. cmp_ids list list specify component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), end (last date compute history, format \"YYYY-MM-DD\") id (name component, see tramoseats_dictionary()). many series periods start end exported. See example. context context specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_revisions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Revisions History — tramoseats_revisions","text":"","code":"s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- tramoseats(s) data_ids <- list(   # Get the coefficient of the trading-day coefficient from 2005-jan   list(start = \"2005-01-01\", id = \"regression.td(1)\"),   # Get the ljung-box statistics on residuals from 2010-jan   list(start = \"2010-01-01\", id = \"residuals.lb\")) ts_ids <- list(   # Get the SA component estimates of 2010-jan from 2010-jan   list(period = \"2010-01-01\", start = \"2010-01-01\", id = \"sa\"),   # Get the irregular component estimates of 2010-jan from 2015-jan   list(period = \"2010-01-01\", start = \"2015-01-01\", id = \"i\")) cmp_ids <- list(   # Get the SA component estimates (full time series) 2010-jan to 2020-jan   list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"sa\"),   # Get the trend component estimates (full time series)  2010-jan to 2020-jan   list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"t\")) rh <- tramoseats_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"Set functions(tramoseats_spec(),tramo_spec()) create default specifications associated TRAMO-SEATS seasonal adjustment method. Specification creation can restricted tramo part tramo_spec() function.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"","code":"tramo_spec(name = c(\"trfull\", \"tr0\", \"tr1\", \"tr2\", \"tr3\", \"tr4\", \"tr5\"))  tramoseats_spec(   name = c(\"rsafull\", \"rsa0\", \"rsa1\", \"rsa2\", \"rsa3\", \"rsa4\", \"rsa5\") )"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"name name predefined specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"object class \"JD3_TRAMOSEATS_SPEC\" (tramoseats_spec()) \"JD3_TRAMO_SPEC\" (tramo_spec()).","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"Without argument tramo_spec() yields TR5 specification without argument tramoseats_spec()  yields RSA5 specification available predefined 'JDemetra+' model specifications described table :","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/tramoseats_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TRAMO/TRAMO-SEATS Default Specification — tramoseats_spec","text":"","code":"init_spec <- tramoseats_spec() init_spec <- tramo_spec() init_spec <- tramoseats_spec(\"rsa3\") init_spec <- tramo_spec(\"tr3\")"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":null,"dir":"Reference","previous_headings":"","what":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"Function generating comprehensive list available output variables estimation process  tramo() tramoseats() functions. items available default estimation output remainder can added using userdefined parameter. User-defined objects can retrieved list lists generated estimation process","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"","code":"userdefined_variables_tramoseats(x = c(\"TRAMO-SEATS\", \"TRAMO\"))"},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"x character indicate estimation function output items list displayed.","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"vector containing names available output objects (series, diagnostics, parameters)","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":"https://rjdemetra.github.io/rjd3tramoseats/reference/userdefined_variables_tramoseats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display list of all available output objects (series, parameters, diagnostics) — userdefined_variables_tramoseats","text":"","code":"userdefined_variables_tramoseats(\"tramoseats\") #>   [1] \"adjust\"                               #>   [2] \"arima.bd\"                             #>   [3] \"arima.bp\"                             #>   [4] \"arima.bphi(*)\"                        #>   [5] \"arima.bq\"                             #>   [6] \"arima.btheta(*)\"                      #>   [7] \"arima.d\"                              #>   [8] \"arima.p\"                              #>   [9] \"arima.phi(*)\"                         #>  [10] \"arima.q\"                              #>  [11] \"arima.theta(*)\"                       #>  [12] \"benchmarking.original\"                #>  [13] \"benchmarking.result\"                  #>  [14] \"benchmarking.target\"                  #>  [15] \"cal\"                                  #>  [16] \"cal_b\"                                #>  [17] \"cal_b(?)\"                             #>  [18] \"cal_f\"                                #>  [19] \"cal_f(?)\"                             #>  [20] \"decomposition.finalmodel.ac\"          #>  [21] \"decomposition.finalmodel.bd\"          #>  [22] \"decomposition.finalmodel.bp\"          #>  [23] \"decomposition.finalmodel.bphi(*)\"     #>  [24] \"decomposition.finalmodel.bq\"          #>  [25] \"decomposition.finalmodel.btheta(*)\"   #>  [26] \"decomposition.finalmodel.d\"           #>  [27] \"decomposition.finalmodel.p\"           #>  [28] \"decomposition.finalmodel.phi(*)\"      #>  [29] \"decomposition.finalmodel.q\"           #>  [30] \"decomposition.finalmodel.spectrum\"    #>  [31] \"decomposition.finalmodel.theta(*)\"    #>  [32] \"decomposition.i_cmp\"                  #>  [33] \"decomposition.i_cmp_b\"                #>  [34] \"decomposition.i_cmp_eb\"               #>  [35] \"decomposition.i_cmp_ef\"               #>  [36] \"decomposition.i_cmp_f\"                #>  [37] \"decomposition.i_lin\"                  #>  [38] \"decomposition.i_lin_b\"                #>  [39] \"decomposition.i_lin_e\"                #>  [40] \"decomposition.i_lin_eb\"               #>  [41] \"decomposition.i_lin_ef\"               #>  [42] \"decomposition.i_lin_f\"                #>  [43] \"decomposition.initialmodel.ac\"        #>  [44] \"decomposition.initialmodel.bd\"        #>  [45] \"decomposition.initialmodel.bp\"        #>  [46] \"decomposition.initialmodel.bphi(*)\"   #>  [47] \"decomposition.initialmodel.bq\"        #>  [48] \"decomposition.initialmodel.btheta(*)\" #>  [49] \"decomposition.initialmodel.d\"         #>  [50] \"decomposition.initialmodel.p\"         #>  [51] \"decomposition.initialmodel.phi(*)\"    #>  [52] \"decomposition.initialmodel.q\"         #>  [53] \"decomposition.initialmodel.spectrum\"  #>  [54] \"decomposition.initialmodel.theta(*)\"  #>  [55] \"decomposition.s_cmp\"                  #>  [56] \"decomposition.s_cmp_b\"                #>  [57] \"decomposition.s_cmp_eb\"               #>  [58] \"decomposition.s_cmp_ef\"               #>  [59] \"decomposition.s_cmp_f\"                #>  [60] \"decomposition.s_lin\"                  #>  [61] \"decomposition.s_lin_b\"                #>  [62] \"decomposition.s_lin_e\"                #>  [63] \"decomposition.s_lin_eb\"               #>  [64] \"decomposition.s_lin_ef\"               #>  [65] \"decomposition.s_lin_f\"                #>  [66] \"decomposition.sa_cmp\"                 #>  [67] \"decomposition.sa_cmp_b\"               #>  [68] \"decomposition.sa_cmp_eb\"              #>  [69] \"decomposition.sa_cmp_ef\"              #>  [70] \"decomposition.sa_cmp_f\"               #>  [71] \"decomposition.sa_lin\"                 #>  [72] \"decomposition.sa_lin_b\"               #>  [73] \"decomposition.sa_lin_e\"               #>  [74] \"decomposition.sa_lin_eb\"              #>  [75] \"decomposition.sa_lin_ef\"              #>  [76] \"decomposition.sa_lin_f\"               #>  [77] \"decomposition.t_cmp\"                  #>  [78] \"decomposition.t_cmp_b\"                #>  [79] \"decomposition.t_cmp_eb\"               #>  [80] \"decomposition.t_cmp_ef\"               #>  [81] \"decomposition.t_cmp_f\"                #>  [82] \"decomposition.t_lin\"                  #>  [83] \"decomposition.t_lin_b\"                #>  [84] \"decomposition.t_lin_e\"                #>  [85] \"decomposition.t_lin_eb\"               #>  [86] \"decomposition.t_lin_ef\"               #>  [87] \"decomposition.t_lin_f\"                #>  [88] \"decomposition.y_cmp\"                  #>  [89] \"decomposition.y_cmp_b\"                #>  [90] \"decomposition.y_cmp_eb\"               #>  [91] \"decomposition.y_cmp_ef\"               #>  [92] \"decomposition.y_cmp_f\"                #>  [93] \"decomposition.y_lin\"                  #>  [94] \"decomposition.y_lin_b\"                #>  [95] \"decomposition.y_lin_eb\"               #>  [96] \"decomposition.y_lin_ef\"               #>  [97] \"decomposition.y_lin_f\"                #>  [98] \"det\"                                  #>  [99] \"det_b(?)\"                             #> [100] \"det_f(?)\"                             #> [101] \"det_i\"                                #> [102] \"det_i_b(?)\"                           #> [103] \"det_i_f(?)\"                           #> [104] \"det_s\"                                #> [105] \"det_s_b(?)\"                           #> [106] \"det_s_f(?)\"                           #> [107] \"det_t\"                                #> [108] \"det_t_b(?)\"                           #> [109] \"det_t_f(?)\"                           #> [110] \"diagnostics.fcast-insample-mean\"      #> [111] \"diagnostics.fcast-outsample-mean\"     #> [112] \"diagnostics.fcast-outsample-variance\" #> [113] \"diagnostics.seas-i-combined\"          #> [114] \"diagnostics.seas-i-combined3\"         #> [115] \"diagnostics.seas-i-evolutive\"         #> [116] \"diagnostics.seas-i-f\"                 #> [117] \"diagnostics.seas-i-friedman\"          #> [118] \"diagnostics.seas-i-kw\"                #> [119] \"diagnostics.seas-i-periodogram\"       #> [120] \"diagnostics.seas-i-qs\"                #> [121] \"diagnostics.seas-i-spectralpeaks\"     #> [122] \"diagnostics.seas-i-stable\"            #> [123] \"diagnostics.seas-lin-combined\"        #> [124] \"diagnostics.seas-lin-evolutive\"       #> [125] \"diagnostics.seas-lin-f\"               #> [126] \"diagnostics.seas-lin-friedman\"        #> [127] \"diagnostics.seas-lin-kw\"              #> [128] \"diagnostics.seas-lin-periodogram\"     #> [129] \"diagnostics.seas-lin-qs\"              #> [130] \"diagnostics.seas-lin-spectralpeaks\"   #> [131] \"diagnostics.seas-lin-stable\"          #> [132] \"diagnostics.seas-res-combined\"        #> [133] \"diagnostics.seas-res-combined3\"       #> [134] \"diagnostics.seas-res-evolutive\"       #> [135] \"diagnostics.seas-res-f\"               #> [136] \"diagnostics.seas-res-friedman\"        #> [137] \"diagnostics.seas-res-kw\"              #> [138] \"diagnostics.seas-res-periodogram\"     #> [139] \"diagnostics.seas-res-qs\"              #> [140] \"diagnostics.seas-res-spectralpeaks\"   #> [141] \"diagnostics.seas-res-stable\"          #> [142] \"diagnostics.seas-sa-ac1\"              #> [143] \"diagnostics.seas-sa-combined\"         #> [144] \"diagnostics.seas-sa-combined3\"        #> [145] \"diagnostics.seas-sa-evolutive\"        #> [146] \"diagnostics.seas-sa-f\"                #> [147] \"diagnostics.seas-sa-friedman\"         #> [148] \"diagnostics.seas-sa-kw\"               #> [149] \"diagnostics.seas-sa-periodogram\"      #> [150] \"diagnostics.seas-sa-qs\"               #> [151] \"diagnostics.seas-sa-spectralpeaks\"    #> [152] \"diagnostics.seas-sa-stable\"           #> [153] \"diagnostics.seas-si-combined\"         #> [154] \"diagnostics.seas-si-combined3\"        #> [155] \"diagnostics.seas-si-evolutive\"        #> [156] \"diagnostics.seas-si-stable\"           #> [157] \"diagnostics.td-i-all\"                 #> [158] \"diagnostics.td-i-last\"                #> [159] \"diagnostics.td-res-all\"               #> [160] \"diagnostics.td-res-last\"              #> [161] \"diagnostics.td-sa-all\"                #> [162] \"diagnostics.td-sa-last\"               #> [163] \"ee\"                                   #> [164] \"ee_b(?)\"                              #> [165] \"ee_f(?)\"                              #> [166] \"i\"                                    #> [167] \"i_b\"                                  #> [168] \"i_eb\"                                 #> [169] \"i_ef\"                                 #> [170] \"i_f\"                                  #> [171] \"l\"                                    #> [172] \"likelihood.adjustedll\"                #> [173] \"likelihood.aic\"                       #> [174] \"likelihood.aicc\"                      #> [175] \"likelihood.bic\"                       #> [176] \"likelihood.bic2\"                      #> [177] \"likelihood.bicc\"                      #> [178] \"likelihood.df\"                        #> [179] \"likelihood.hannanquinn\"               #> [180] \"likelihood.ll\"                        #> [181] \"likelihood.neffectiveobs\"             #> [182] \"likelihood.nobs\"                      #> [183] \"likelihood.nparams\"                   #> [184] \"likelihood.ssqerr\"                    #> [185] \"log\"                                  #> [186] \"mhe\"                                  #> [187] \"mhe_b(?)\"                             #> [188] \"mhe_f(?)\"                             #> [189] \"mode\"                                 #> [190] \"omhe\"                                 #> [191] \"omhe_b(?)\"                            #> [192] \"omhe_f(?)\"                            #> [193] \"out\"                                  #> [194] \"out_b(?)\"                             #> [195] \"out_f(?)\"                             #> [196] \"out_i\"                                #> [197] \"out_i_b(?)\"                           #> [198] \"out_i_f(?)\"                           #> [199] \"out_s\"                                #> [200] \"out_s_b(?)\"                           #> [201] \"out_s_f(?)\"                           #> [202] \"out_t\"                                #> [203] \"out_t_b(?)\"                           #> [204] \"out_t_f(?)\"                           #> [205] \"period\"                               #> [206] \"reg_i\"                                #> [207] \"reg_i_b(?)\"                           #> [208] \"reg_i_f(?)\"                           #> [209] \"reg_s\"                                #> [210] \"reg_s_b(?)\"                           #> [211] \"reg_s_f(?)\"                           #> [212] \"reg_sa\"                               #> [213] \"reg_sa_b(?)\"                          #> [214] \"reg_sa_f(?)\"                          #> [215] \"reg_t\"                                #> [216] \"reg_t_b(?)\"                           #> [217] \"reg_t_f(?)\"                           #> [218] \"reg_u\"                                #> [219] \"reg_u_b(?)\"                           #> [220] \"reg_u_f(?)\"                           #> [221] \"reg_y\"                                #> [222] \"reg_y_b(?)\"                           #> [223] \"reg_y_f(?)\"                           #> [224] \"regression.description\"               #> [225] \"regression.details.coefficients\"      #> [226] \"regression.details.covar\"             #> [227] \"regression.details.covar-ml\"          #> [228] \"regression.easter\"                    #> [229] \"regression.espan.end\"                 #> [230] \"regression.espan.missing\"             #> [231] \"regression.espan.n\"                   #> [232] \"regression.espan.start\"               #> [233] \"regression.leaster\"                   #> [234] \"regression.lp\"                        #> [235] \"regression.mean\"                      #> [236] \"regression.missing(*)\"                #> [237] \"regression.ml.parameters\"             #> [238] \"regression.ml.pcorr\"                  #> [239] \"regression.ml.pcovar\"                 #> [240] \"regression.ml.pcovar-ml\"              #> [241] \"regression.ml.pscore\"                 #> [242] \"regression.mu\"                        #> [243] \"regression.nao\"                       #> [244] \"regression.nlp\"                       #> [245] \"regression.nls\"                       #> [246] \"regression.nmh\"                       #> [247] \"regression.nout\"                      #> [248] \"regression.nso\"                       #> [249] \"regression.ntc\"                       #> [250] \"regression.ntd\"                       #> [251] \"regression.nusers\"                    #> [252] \"regression.out(*)\"                    #> [253] \"regression.outlier(*)\"                #> [254] \"regression.td(*)\"                     #> [255] \"regression.type\"                      #> [256] \"regression.user(*)\"                   #> [257] \"residuals.bp\"                         #> [258] \"residuals.bp2\"                        #> [259] \"residuals.doornikhansen\"              #> [260] \"residuals.kurtosis\"                   #> [261] \"residuals.lb\"                         #> [262] \"residuals.lb2\"                        #> [263] \"residuals.lruns\"                      #> [264] \"residuals.ludruns\"                    #> [265] \"residuals.mean\"                       #> [266] \"residuals.nruns\"                      #> [267] \"residuals.nudruns\"                    #> [268] \"residuals.res\"                        #> [269] \"residuals.seasbp\"                     #> [270] \"residuals.seaslb\"                     #> [271] \"residuals.ser\"                        #> [272] \"residuals.skewness\"                   #> [273] \"residuals.tsres\"                      #> [274] \"residuals.type\"                       #> [275] \"s\"                                    #> [276] \"s_b\"                                  #> [277] \"s_eb\"                                 #> [278] \"s_ef\"                                 #> [279] \"s_f\"                                  #> [280] \"sa\"                                   #> [281] \"sa_b\"                                 #> [282] \"sa_eb\"                                #> [283] \"sa_ef\"                                #> [284] \"sa_f\"                                 #> [285] \"seasonal\"                             #> [286] \"seats.ar_root(*)\"                     #> [287] \"seats.ivar-estimate\"                  #> [288] \"seats.ivar-estimator\"                 #> [289] \"seats.ivar-pvalue\"                    #> [290] \"seats.ma_root(*)\"                     #> [291] \"seats.model_changed\"                  #> [292] \"seats.parameters_cutoff\"              #> [293] \"seats.savar-estimate\"                 #> [294] \"seats.savar-estimator\"                #> [295] \"seats.savar-pvalue\"                   #> [296] \"seats.seasonality\"                    #> [297] \"seats.sicorr-estimate\"                #> [298] \"seats.sicorr-estimator\"               #> [299] \"seats.sicorr-pvalue\"                  #> [300] \"seats.svar-estimate\"                  #> [301] \"seats.svar-estimator\"                 #> [302] \"seats.svar-pvalue\"                    #> [303] \"seats.ticorr-estimate\"                #> [304] \"seats.ticorr-estimator\"               #> [305] \"seats.ticorr-pvalue\"                  #> [306] \"seats.tscorr-estimate\"                #> [307] \"seats.tscorr-estimator\"               #> [308] \"seats.tscorr-pvalue\"                  #> [309] \"seats.tvar-estimate\"                  #> [310] \"seats.tvar-estimator\"                 #> [311] \"seats.tvar-pvalue\"                    #> [312] \"span.end\"                             #> [313] \"span.missing\"                         #> [314] \"span.n\"                               #> [315] \"span.start\"                           #> [316] \"t\"                                    #> [317] \"t_b\"                                  #> [318] \"t_eb\"                                 #> [319] \"t_ef\"                                 #> [320] \"t_f\"                                  #> [321] \"tde\"                                  #> [322] \"tde_b(?)\"                             #> [323] \"tde_f(?)\"                             #> [324] \"variancedecomposition.cycle\"          #> [325] \"variancedecomposition.irregular\"      #> [326] \"variancedecomposition.others\"         #> [327] \"variancedecomposition.seasonality\"    #> [328] \"variancedecomposition.tdh\"            #> [329] \"variancedecomposition.total\"          #> [330] \"y\"                                    #> [331] \"y_b\"                                  #> [332] \"y_b(?)\"                               #> [333] \"y_eb(?)\"                              #> [334] \"y_ef(?)\"                              #> [335] \"y_f\"                                  #> [336] \"y_f(?)\"                               #> [337] \"yc\"                                   #> [338] \"ycal\"                                 #> [339] \"ycal_f(?)\"                            userdefined_variables_tramoseats(\"tramo\") #>   [1] \"adjust\"                          \"arima.bd\"                        #>   [3] \"arima.bp\"                        \"arima.bphi(*)\"                   #>   [5] \"arima.bq\"                        \"arima.btheta(*)\"                 #>   [7] \"arima.d\"                         \"arima.p\"                         #>   [9] \"arima.phi(*)\"                    \"arima.q\"                         #>  [11] \"arima.theta(*)\"                  \"cal\"                             #>  [13] \"cal_b(?)\"                        \"cal_f(?)\"                        #>  [15] \"det\"                             \"det_b(?)\"                        #>  [17] \"det_f(?)\"                        \"det_i\"                           #>  [19] \"det_i_b(?)\"                      \"det_i_f(?)\"                      #>  [21] \"det_s\"                           \"det_s_b(?)\"                      #>  [23] \"det_s_f(?)\"                      \"det_t\"                           #>  [25] \"det_t_b(?)\"                      \"det_t_f(?)\"                      #>  [27] \"ee\"                              \"ee_b(?)\"                         #>  [29] \"ee_f(?)\"                         \"l\"                               #>  [31] \"likelihood.adjustedll\"           \"likelihood.aic\"                  #>  [33] \"likelihood.aicc\"                 \"likelihood.bic\"                  #>  [35] \"likelihood.bic2\"                 \"likelihood.bicc\"                 #>  [37] \"likelihood.df\"                   \"likelihood.hannanquinn\"          #>  [39] \"likelihood.ll\"                   \"likelihood.neffectiveobs\"        #>  [41] \"likelihood.nobs\"                 \"likelihood.nparams\"              #>  [43] \"likelihood.ssqerr\"               \"log\"                             #>  [45] \"mhe\"                             \"mhe_b(?)\"                        #>  [47] \"mhe_f(?)\"                        \"omhe\"                            #>  [49] \"omhe_b(?)\"                       \"omhe_f(?)\"                       #>  [51] \"out\"                             \"out_b(?)\"                        #>  [53] \"out_f(?)\"                        \"out_i\"                           #>  [55] \"out_i_b(?)\"                      \"out_i_f(?)\"                      #>  [57] \"out_s\"                           \"out_s_b(?)\"                      #>  [59] \"out_s_f(?)\"                      \"out_t\"                           #>  [61] \"out_t_b(?)\"                      \"out_t_f(?)\"                      #>  [63] \"period\"                          \"reg_i\"                           #>  [65] \"reg_i_b(?)\"                      \"reg_i_f(?)\"                      #>  [67] \"reg_s\"                           \"reg_s_b(?)\"                      #>  [69] \"reg_s_f(?)\"                      \"reg_sa\"                          #>  [71] \"reg_sa_b(?)\"                     \"reg_sa_f(?)\"                     #>  [73] \"reg_t\"                           \"reg_t_b(?)\"                      #>  [75] \"reg_t_f(?)\"                      \"reg_u\"                           #>  [77] \"reg_u_b(?)\"                      \"reg_u_f(?)\"                      #>  [79] \"reg_y\"                           \"reg_y_b(?)\"                      #>  [81] \"reg_y_f(?)\"                      \"regression.description\"          #>  [83] \"regression.details.coefficients\" \"regression.details.covar\"        #>  [85] \"regression.details.covar-ml\"     \"regression.easter\"               #>  [87] \"regression.espan.end\"            \"regression.espan.missing\"        #>  [89] \"regression.espan.n\"              \"regression.espan.start\"          #>  [91] \"regression.leaster\"              \"regression.lp\"                   #>  [93] \"regression.mean\"                 \"regression.missing(*)\"           #>  [95] \"regression.ml.parameters\"        \"regression.ml.pcorr\"             #>  [97] \"regression.ml.pcovar\"            \"regression.ml.pcovar-ml\"         #>  [99] \"regression.ml.pscore\"            \"regression.mu\"                   #> [101] \"regression.nao\"                  \"regression.nlp\"                  #> [103] \"regression.nls\"                  \"regression.nmh\"                  #> [105] \"regression.nout\"                 \"regression.nso\"                  #> [107] \"regression.ntc\"                  \"regression.ntd\"                  #> [109] \"regression.nusers\"               \"regression.out(*)\"               #> [111] \"regression.outlier(*)\"           \"regression.td(*)\"                #> [113] \"regression.type\"                 \"regression.user(*)\"              #> [115] \"residuals.bp\"                    \"residuals.bp2\"                   #> [117] \"residuals.doornikhansen\"         \"residuals.kurtosis\"              #> [119] \"residuals.lb\"                    \"residuals.lb2\"                   #> [121] \"residuals.lruns\"                 \"residuals.ludruns\"               #> [123] \"residuals.mean\"                  \"residuals.nruns\"                 #> [125] \"residuals.nudruns\"               \"residuals.res\"                   #> [127] \"residuals.seasbp\"                \"residuals.seaslb\"                #> [129] \"residuals.ser\"                   \"residuals.skewness\"              #> [131] \"residuals.tsres\"                 \"residuals.type\"                  #> [133] \"span.end\"                        \"span.missing\"                    #> [135] \"span.n\"                          \"span.start\"                      #> [137] \"tde\"                             \"tde_b(?)\"                        #> [139] \"tde_f(?)\"                        \"y\"                               #> [141] \"y_b(?)\"                          \"y_eb(?)\"                         #> [143] \"y_ef(?)\"                         \"y_f(?)\"                          #> [145] \"yc\"                              \"ycal\"                            #> [147] \"ycal_f(?)\"                       y = rjd3toolkit::ABS$X0.2.09.10.M m <- tramoseats(y,\"rsafull\", userdefined=c(\"ycal\",\"variancedecomposition.seasonality\")) m$user_defined$ycal #>            Jan       Feb       Mar       Apr       May       Jun       Jul #> 1982                                443.8278  510.6464  441.5556  447.2441 #> 1983  385.2708  381.9008  463.5100  496.9624  524.2277  456.2137  471.7869 #> 1984  425.7682  401.8572  484.2305  490.2518  587.0210  494.0008  557.9031 #> 1985  506.9057  457.1696  543.2305  576.5562  692.6467  564.5811  606.3516 #> 1986  564.2652  483.1348  542.3265  606.3810  732.1905  570.9575  651.1328 #> 1987  598.0465  518.4960  631.6257  648.5345  719.3164  674.5692  713.0216 #> 1988  635.6984  540.2656  665.7617  706.5474  760.5717  712.3406  688.5893 #> 1989  648.3545  557.6964  693.4493  718.9404  769.3060  777.8438  726.5485 #> 1990  682.1958  592.2493  726.0960  748.6074  800.6617  821.7550  729.2576 #> 1991  681.3724  610.9402  706.5284  771.9465  819.1489  750.5975  799.1810 #> 1992  731.9128  635.8665  738.5989  799.8688  885.1420  831.0129  779.5861 #> 1993  767.7745  649.6355  777.8968  810.9673  921.1143  882.6137  751.5766 #> 1994  757.6383  671.2563  806.5525  821.0640  932.2342  900.3907  788.7294 #> 1995  772.4813  689.5431  822.8058  887.8325  932.6628  897.0479  894.6980 #> 1996  838.0084  705.3053  838.9181  878.2254  952.2779  913.0980  913.3497 #> 1997  831.7101  734.9064  849.9486  866.2586  969.1100  856.5394  953.4305 #> 1998  893.6114  723.5909  866.4191  933.3085  977.4240  854.5549 1030.9091 #> 1999  949.0141  746.0200  916.1835  935.0630 1027.3889  889.8768 1027.2683 #> 2000  939.7073  770.2115  914.8035 1004.1523 1058.4999 1152.9657  873.0570 #> 2001  977.6262  823.0063 1016.5144 1045.4950 1103.0575 1011.7933 1130.1799 #> 2002 1009.1970  850.0829 1025.5075 1109.4281 1177.8851 1148.6583 1082.0378 #> 2003 1087.2862  909.7928 1138.5197 1185.9589 1205.1528 1277.1743 1160.4931 #> 2004 1152.3525  979.4507 1167.1031 1231.9334 1300.7971 1377.1002 1272.1761 #> 2005 1266.2527 1048.0045 1245.3569 1168.0058 1297.9410 1434.7448 1299.7060 #> 2006 1225.5594 1048.4086 1221.7328 1321.5573 1275.0684 1413.6319 1434.6055 #> 2007 1274.7666 1057.8046 1330.3101 1310.4905 1340.2615 1472.4263 1501.3282 #> 2008 1372.3665 1108.6542 1339.1719 1424.9903 1384.4011 1446.0679 1553.5095 #> 2009 1413.5290 1067.5036 1460.2463 1451.8667 1530.7133 1509.6064 1553.5344 #> 2010 1480.0388 1123.3743 1399.7360 1363.2575 1450.0489 1543.5542 1540.1718 #> 2011 1435.2151 1129.0321 1330.6594 1417.5987 1446.5030 1452.2080 1543.9109 #> 2012 1398.7619 1097.1817 1347.8930 1399.6272 1430.3912 1595.7290 1461.3900 #> 2013 1371.9738 1125.0919 1372.6810 1381.6006 1426.1420 1545.0108 1398.7931 #> 2014 1435.1426 1075.8893 1347.4271 1399.8856 1375.3413 1507.9715 1474.2779 #> 2015 1433.2073 1064.6748 1427.4659 1399.2055 1439.3380 1489.8874 1524.0601 #> 2016 1530.5119 1133.0774 1389.6337 1468.9923 1488.3919 1574.7464 1479.2329 #> 2017 1466.6261 1103.6731 1389.9049 1492.0682 1460.9572 1540.4532 1468.6419 #>            Aug       Sep       Oct       Nov       Dec #> 1982  450.1007  458.8776  456.0708  526.0734  879.5812 #> 1983  460.8996  477.5088  493.0695  565.8239  938.8132 #> 1984  497.8985  525.3881  559.8794  616.4978 1056.0404 #> 1985  567.4574  580.9652  625.5077  688.1853 1219.1939 #> 1986  585.4268  665.9170  658.3259  704.3698 1375.9594 #> 1987  611.2314  668.9000  690.7880  766.7792 1433.5330 #> 1988  654.7350  715.5213  741.2806  865.8987 1529.4559 #> 1989  710.6302  748.2078  771.7626  911.4408 1581.0856 #> 1990  751.1008  744.7688  791.2345  921.4822 1588.1246 #> 1991  750.5050  765.0252  842.9775  932.6189 1643.0114 #> 1992  735.1845  815.6541  879.1936  999.3303 1620.8613 #> 1993  748.1487  833.3991  893.5038  975.8868 1691.8066 #> 1994  803.5063  877.4608  949.5626  995.3407 1770.5644 #> 1995  857.9991  845.8430  945.1704 1053.1197 1871.0286 #> 1996  865.9448  860.1504  916.7110 1067.7682 1886.6448 #> 1997  842.2248  944.7993  966.2238 1116.8014 1890.1712 #> 1998  883.8267  934.6492  994.9257 1173.7953 1939.7515 #> 1999  923.5073  988.8906 1051.4713 1214.5264 2046.9821 #> 2000  990.8373 1041.1135 1098.9686 1216.0109 2193.7135 #> 2001  952.4757 1017.9143 1138.6695 1330.3374 2305.8338 #> 2002 1038.3739 1093.7333 1189.3582 1475.9803 2401.0163 #> 2003 1109.3995 1205.4718 1301.7168 1535.7652 2438.4828 #> 2004 1197.9414 1300.2683 1368.4138 1546.0261 2473.4697 #> 2005 1217.8332 1264.0580 1335.4430 1532.6098 2470.5097 #> 2006 1216.6523 1203.5098 1380.4868 1601.5825 2630.5421 #> 2007 1301.1234 1359.9994 1449.0877 1669.7476 2790.9783 #> 2008 1277.8444 1391.6951 1436.5273 1684.1964 2903.1042 #> 2009 1362.9769 1392.8203 1494.6778 1791.6963 2744.2422 #> 2010 1359.3370 1402.9756 1475.8075 1715.8505 2722.3201 #> 2011 1301.1210 1331.2277 1427.1887 1651.6050 2659.9868 #> 2012 1344.1480 1374.5712 1451.0995 1654.9089 2797.3792 #> 2013 1302.6678 1402.5291 1436.6748 1647.5577 2798.2425 #> 2014 1263.5387 1364.6319 1462.5398 1696.0548 2773.1430 #> 2015 1353.2232 1393.0193 1525.8515 1785.4028 2860.5860 #> 2016 1301.5234 1378.5137 1521.3728 1675.7821 2776.7905 #> 2017 1279.3896                                         m$user_defined$variancedecomposition.seasonality #> [1] 0.9704556"}]