rjd3tramoseats offers full access to options and outputs of TRAMO-SEATS
(rjd3tramoseats::tramoseats()), including TRAMO modelling
(rjd3tramoseats::tramo()) and SEATS decomposition
(rjd3tramoseats::seats_decompose()).
A specification can be created with rjd3tramoseats::tramo_spec() or
rjd3tramoseats::tramoseats_spec() and can be modified with the
following functions:
-
for pre-processing:
rjd3tramoseats::set_arima(),rjd3tramoseats::set_automodel(),rjd3tramoseats::set_basic(),rjd3tramoseats::set_easter(),rjd3tramoseats::set_estimate(),rjd3tramoseats::set_outlier(),rjd3tramoseats::set_tradingdays(),rjd3tramoseats::set_transform(),rjd3tramoseats::add_outlier(),rjd3tramoseats::remove_outlier(),rjd3tramoseats::add_ramp(),rjd3tramoseats::remove_ramp(),rjd3tramoseats::add_usrdefvar(); -
for decomposition:
rjd3tramoseats::set_seats(); -
for benchmarking:
rjd3tramoseats::set_benchmarking().
Running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained here
To get the current stable version (from the latest release):
- From GitHub:
# install.packages("remotes")
remotes::install_github("rjdverse/rjd3toolkit@*release")
remotes::install_github("rjdverse/rjd3tramoseats@*release")- From r-universe:
install.packages("rjd3tramoseats", repos = c("https://rjdverse.r-universe.dev", "https://cloud.r-project.org"))You can install the development version of rjd3tramoseats from GitHub with:
# install.packages("remotes")
remotes::install_github("rjdverse/rjd3tramoseats")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 0.166481
#> 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 0.483748
#> saturday 0.0153509 0.0035171 4.365 1.62e-05 ***
#> lp 0.0410667 0.0101178 4.059 5.94e-05 ***
#> easter 0.0503888 0.0072698 6.931 1.69e-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
To get the final components you can use the function
rjd3toolkit::sa_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 0.9900960Any contribution is welcome and should be done through pull requests and/or issues. pull requests should include updated tests and updated documentation. If functionality is changed, docstrings should be added or updated.
The code of this project is licensed under the European Union Public Licence (EUPL).