From 02e7926d967cc2acb7a88a62f2e331caedf54f8b Mon Sep 17 00:00:00 2001 From: Tim Januschowski Date: Sun, 13 Oct 2019 23:34:01 +0200 Subject: [PATCH 1/5] add more references in README for related scientific papers --- README.md | 180 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 180 insertions(+) diff --git a/README.md b/README.md index 4e80c21697..4a1310d53e 100644 --- a/README.md +++ b/README.md @@ -141,3 +141,183 @@ the following reference to the associated year={2019} } ``` + +## Further Papers + +We encourage you to also check out work by the group behind +GluonTS. They are grouped according to topic and ordered +chronographically. + +### Methods +A number of the below methods are available in GluonTS. + +Deep Factor models, a global-local forecasting method. +``` +@inproceedings{wang2019deepfactors, + Author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim}, + Booktitle = {International Conference on Machine Learning}, + Pages = {6607--6617}, + Title = {Deep factors for forecasting}, + Year = {2019} +} +``` +DeepAR, an RNN-based probabilistic forecasting model. +``` +@article{flunkert2019deepar, + Author = {Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Tim Januschowski}, + Journal = {International Journal of Forecasting}, + Title = {DeepAR: Probabilistic forecasting with autoregressive recurrent networks}, + Year = {2019} +} +``` +A flexible way to model probabilistic forecasts via spline quantile forecasts. +``` +@inproceedings{gasthaus2019probabilistic, + Author = {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim}, + Booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics}, + Date-Added = {2019-06-26 13:23:32 +0000}, + Date-Modified = {2019-06-26 13:24:07 +0000}, + Pages = {1901--1910}, + Title = {Probabilistic Forecasting with Spline Quantile Function RNNs}, + Year = {2019} +} +``` +Using RNNs to parametrize State Space Models. +``` +@inproceedings{rangapuram2018deep, + Author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim}, + Booktitle = {Advances in Neural Information Processing Systems}, + Date-Added = {2019-06-26 13:38:03 +0000}, + Date-Modified = {2019-06-26 13:38:43 +0000}, + Pages = {7785--7794}, + Title = {Deep state space models for time series forecasting}, + Year = {2018} +} +``` +A scalable state space model. +``` +@inproceedings{seeger2016bayesian, + Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin}, + Booktitle = {Advances in Neural Information Processing Systems}, + Date-Added = {2019-06-27 13:17:25 +0000}, + Date-Modified = {2019-06-27 13:18:01 +0000}, + Pages = {4646--4654}, + Title = {Bayesian intermittent demand forecasting for large inventories}, + Year = {2016} +} +``` + + + +### Tutorials +Tutorials are available in bibtex and with accompanying material, + in particular slides, linked from below. +[Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) +``` +@inproceedings{faloutsos19forecasting2, + author = {Faloutsos, Christos and + Flunkert, Valentin and + Gasthaus, Jan and + Januschowski, Tim and + Wang, Yuyang}, + title = {Forecasting Big Time Series: Theory and Practice}, + booktitle = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on + Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK, + USA, August 4-8, 2019.}, + year = {2019} + } +``` +[Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) +``` +@inproceedings{faloutsos2019forecasting, + author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, + title = {Classical and Contemporary Approaches to Big Time Series Forecasting}, + booktitle = {Proceedings of the 2019 International Conference on Management of Data}, + series = {SIGMOD '19}, + publisher = {ACM}, + address = {New York, NY, USA}, + year = {2019} +} +``` +[Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) +``` +@article{faloutsos2018forecasting, + Author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, + Date-Added = {2019-07-24 13:47:16 +0000}, + Date-Modified = {2019-07-24 13:48:00 +0000}, + Journal = {Proceedings of the VLDB Endowment}, + Number = {12}, + Pages = {2102--2105}, + Title = {Forecasting big time series: old and new}, + Volume = {11}, + Year = {2018} +} +``` + +### General audience +An overview of forecasting libraries in Python. +``` +@article{januschowski19opensource, + title={Open-Source Forecasting Tools in Python}, + author={Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang}, + journal={Foresight: The International Journal of Applied Forecasting}, + year={2019} +} +``` +A commentary on the M4 competition and its classification of the participating methods +into 'statistical' and 'ML' methods. The article proposes alternative criteria. +``` +@article{januschowski19criteria, +title = "Criteria for classifying forecasting methods", +author = "Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent" +journal = "International Journal of Forecasting", +year = "2019" +} +``` +The business forecasting problem landscape can be divided into +strategic, tactical and operational forecasting problems. +``` +@article{januschowski18classification, + title={A Classification of Business Forecasting Problems}, + author={Januschowski, Tim and Kolassa, Stephan}, + journal={Foresight: The International Journal of Applied Forecasting}, + year={2019}, + volume={52}, + pages={36-43} +} +``` +A two-part article introducing deep learning for forecasting. +``` +@article{januschowski18deeplearning2, +title = {Deep Learning for Forecasting: Current Trends and Challenges}, +journal = {Foresight: The International Journal of Applied Forecasting}, +year = "2018", +author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent}, +volume = {51}, +pages = {42-47} +} +``` +``` +@article{januschowski18deeplearning, + title = {Deep Learning for Forecasting}, + author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent}, + journal = {Foresight}, + year = {2018} +} +``` + +### System Aspects +A large-scale retail forecasting system. +``` +@article{bose2017probabilistic, + Author = {B{\"o}se, Joos-Hendrik and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Lange, Dustin and Salinas, David and Schelter, Sebastian and Seeger, Matthias and Wang, Yuyang}, + Date-Added = {2019-06-27 14:12:57 +0000}, + Date-Modified = {2019-06-27 14:13:35 +0000}, + Journal = {Proceedings of the VLDB Endowment}, + Number = {12}, + Pages = {1694--1705}, + Title = {Probabilistic demand forecasting at scale}, + Volume = {10}, + Year = {2017} +} +``` \ No newline at end of file From 2fd92d7ca922c5ecb00e8d0b859577c1a42ea291 Mon Sep 17 00:00:00 2001 From: Tim Januschowski Date: Mon, 14 Oct 2019 11:59:09 +0200 Subject: [PATCH 2/5] address comments by jaheba --- README.md | 183 ++------------------------------------------------ REFERENCES.md | 183 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 188 insertions(+), 178 deletions(-) create mode 100644 REFERENCES.md diff --git a/README.md b/README.md index 4a1310d53e..0ccfbc56e0 100644 --- a/README.md +++ b/README.md @@ -142,182 +142,9 @@ the following reference to the associated } ``` -## Further Papers +## Further Reading -We encourage you to also check out work by the group behind -GluonTS. They are grouped according to topic and ordered -chronographically. - -### Methods -A number of the below methods are available in GluonTS. - -Deep Factor models, a global-local forecasting method. -``` -@inproceedings{wang2019deepfactors, - Author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim}, - Booktitle = {International Conference on Machine Learning}, - Pages = {6607--6617}, - Title = {Deep factors for forecasting}, - Year = {2019} -} -``` -DeepAR, an RNN-based probabilistic forecasting model. -``` -@article{flunkert2019deepar, - Author = {Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Tim Januschowski}, - Journal = {International Journal of Forecasting}, - Title = {DeepAR: Probabilistic forecasting with autoregressive recurrent networks}, - Year = {2019} -} -``` -A flexible way to model probabilistic forecasts via spline quantile forecasts. -``` -@inproceedings{gasthaus2019probabilistic, - Author = {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim}, - Booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics}, - Date-Added = {2019-06-26 13:23:32 +0000}, - Date-Modified = {2019-06-26 13:24:07 +0000}, - Pages = {1901--1910}, - Title = {Probabilistic Forecasting with Spline Quantile Function RNNs}, - Year = {2019} -} -``` -Using RNNs to parametrize State Space Models. -``` -@inproceedings{rangapuram2018deep, - Author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim}, - Booktitle = {Advances in Neural Information Processing Systems}, - Date-Added = {2019-06-26 13:38:03 +0000}, - Date-Modified = {2019-06-26 13:38:43 +0000}, - Pages = {7785--7794}, - Title = {Deep state space models for time series forecasting}, - Year = {2018} -} -``` -A scalable state space model. -``` -@inproceedings{seeger2016bayesian, - Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin}, - Booktitle = {Advances in Neural Information Processing Systems}, - Date-Added = {2019-06-27 13:17:25 +0000}, - Date-Modified = {2019-06-27 13:18:01 +0000}, - Pages = {4646--4654}, - Title = {Bayesian intermittent demand forecasting for large inventories}, - Year = {2016} -} -``` - - - -### Tutorials -Tutorials are available in bibtex and with accompanying material, - in particular slides, linked from below. -[Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) -``` -@inproceedings{faloutsos19forecasting2, - author = {Faloutsos, Christos and - Flunkert, Valentin and - Gasthaus, Jan and - Januschowski, Tim and - Wang, Yuyang}, - title = {Forecasting Big Time Series: Theory and Practice}, - booktitle = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on - Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK, - USA, August 4-8, 2019.}, - year = {2019} - } -``` -[Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) -``` -@inproceedings{faloutsos2019forecasting, - author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, - title = {Classical and Contemporary Approaches to Big Time Series Forecasting}, - booktitle = {Proceedings of the 2019 International Conference on Management of Data}, - series = {SIGMOD '19}, - publisher = {ACM}, - address = {New York, NY, USA}, - year = {2019} -} -``` -[Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) -``` -@article{faloutsos2018forecasting, - Author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, - Date-Added = {2019-07-24 13:47:16 +0000}, - Date-Modified = {2019-07-24 13:48:00 +0000}, - Journal = {Proceedings of the VLDB Endowment}, - Number = {12}, - Pages = {2102--2105}, - Title = {Forecasting big time series: old and new}, - Volume = {11}, - Year = {2018} -} -``` - -### General audience -An overview of forecasting libraries in Python. -``` -@article{januschowski19opensource, - title={Open-Source Forecasting Tools in Python}, - author={Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang}, - journal={Foresight: The International Journal of Applied Forecasting}, - year={2019} -} -``` -A commentary on the M4 competition and its classification of the participating methods -into 'statistical' and 'ML' methods. The article proposes alternative criteria. -``` -@article{januschowski19criteria, -title = "Criteria for classifying forecasting methods", -author = "Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent" -journal = "International Journal of Forecasting", -year = "2019" -} -``` -The business forecasting problem landscape can be divided into -strategic, tactical and operational forecasting problems. -``` -@article{januschowski18classification, - title={A Classification of Business Forecasting Problems}, - author={Januschowski, Tim and Kolassa, Stephan}, - journal={Foresight: The International Journal of Applied Forecasting}, - year={2019}, - volume={52}, - pages={36-43} -} -``` -A two-part article introducing deep learning for forecasting. -``` -@article{januschowski18deeplearning2, -title = {Deep Learning for Forecasting: Current Trends and Challenges}, -journal = {Foresight: The International Journal of Applied Forecasting}, -year = "2018", -author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent}, -volume = {51}, -pages = {42-47} -} -``` -``` -@article{januschowski18deeplearning, - title = {Deep Learning for Forecasting}, - author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent}, - journal = {Foresight}, - year = {2018} -} -``` - -### System Aspects -A large-scale retail forecasting system. -``` -@article{bose2017probabilistic, - Author = {B{\"o}se, Joos-Hendrik and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Lange, Dustin and Salinas, David and Schelter, Sebastian and Seeger, Matthias and Wang, Yuyang}, - Date-Added = {2019-06-27 14:12:57 +0000}, - Date-Modified = {2019-06-27 14:13:35 +0000}, - Journal = {Proceedings of the VLDB Endowment}, - Number = {12}, - Pages = {1694--1705}, - Title = {Probabilistic demand forecasting at scale}, - Volume = {10}, - Year = {2017} -} -``` \ No newline at end of file +* [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluon-ts/tree/master/REFERENCES.md): a bibliography. +* [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) +* [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) +* [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) diff --git a/REFERENCES.md b/REFERENCES.md new file mode 100644 index 0000000000..370bb69811 --- /dev/null +++ b/REFERENCES.md @@ -0,0 +1,183 @@ +# Scientific Articles +We encourage you to also check out work by the group behind +GluonTS. They are grouped according to topic and ordered +chronographically. + +### Methods +A number of the below methods are available in GluonTS. + +``` +@inproceedings{salinas2019high, + Author = {Salinas, David and Bohlke-Schneider, Michael and Callot, Laurent and Gasthaus, Jan}, + Booktitle = {Advances in Neural Information Processing Systems}, + Title = {High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes}, + Year = {2019} +} +``` + +Deep Factor models, a global-local forecasting method. +``` +@inproceedings{wang2019deepfactors, + Author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim}, + Booktitle = {International Conference on Machine Learning}, + Pages = {6607--6617}, + Title = {Deep factors for forecasting}, + Year = {2019} +} +``` +DeepAR, an RNN-based probabilistic forecasting model. +``` +@article{flunkert2019deepar, + Author = {Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Tim Januschowski}, + Journal = {International Journal of Forecasting}, + Title = {DeepAR: Probabilistic forecasting with autoregressive recurrent networks}, + Year = {2019} +} +``` +A flexible way to model probabilistic forecasts via spline quantile forecasts. +``` +@inproceedings{gasthaus2019probabilistic, + Author = {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim}, + Booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics}, + Pages = {1901--1910}, + Title = {Probabilistic Forecasting with Spline Quantile Function RNNs}, + Year = {2019} +} +``` +Using RNNs to parametrize State Space Models. +``` +@inproceedings{rangapuram2018deep, + Author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim}, + Booktitle = {Advances in Neural Information Processing Systems}, + Pages = {7785--7794}, + Title = {Deep state space models for time series forecasting}, + Year = {2018} +} +``` +A scalable state space model. +``` +@inproceedings{seeger2016bayesian, + Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin}, + Booktitle = {Advances in Neural Information Processing Systems}, + Date-Added = {2019-06-27 13:17:25 +0000}, + Date-Modified = {2019-06-27 13:18:01 +0000}, + Pages = {4646--4654}, + Title = {Bayesian intermittent demand forecasting for large inventories}, + Year = {2016} +} +``` + + + +### Tutorials +Tutorials are available in bibtex and with accompanying material, + in particular slides, linked from below. +[Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) +``` +@inproceedings{faloutsos19forecasting2, + author = {Faloutsos, Christos and + Flunkert, Valentin and + Gasthaus, Jan and + Januschowski, Tim and + Wang, Yuyang}, + title = {Forecasting Big Time Series: Theory and Practice}, + booktitle = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on + Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK, + USA, August 4-8, 2019.}, + year = {2019} + } +``` +[Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) +``` +@inproceedings{faloutsos2019forecasting, + author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, + title = {Classical and Contemporary Approaches to Big Time Series Forecasting}, + booktitle = {Proceedings of the 2019 International Conference on Management of Data}, + series = {SIGMOD '19}, + publisher = {ACM}, + address = {New York, NY, USA}, + year = {2019} +} +``` +[Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) +``` +@article{faloutsos2018forecasting, + Author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, + Date-Added = {2019-07-24 13:47:16 +0000}, + Date-Modified = {2019-07-24 13:48:00 +0000}, + Journal = {Proceedings of the VLDB Endowment}, + Number = {12}, + Pages = {2102--2105}, + Title = {Forecasting big time series: old and new}, + Volume = {11}, + Year = {2018} +} +``` + +### General audience +An overview of forecasting libraries in Python. +``` +@article{januschowski19opensource, + title={Open-Source Forecasting Tools in Python}, + author={Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang}, + journal={Foresight: The International Journal of Applied Forecasting}, + year={2019} +} +``` +A commentary on the M4 competition and its classification of the participating methods +into 'statistical' and 'ML' methods. The article proposes alternative criteria. +``` +@article{januschowski19criteria, +title = "Criteria for classifying forecasting methods", +author = "Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent" +journal = "International Journal of Forecasting", +year = "2019" +} +``` +The business forecasting problem landscape can be divided into +strategic, tactical and operational forecasting problems. +``` +@article{januschowski18classification, + title={A Classification of Business Forecasting Problems}, + author={Januschowski, Tim and Kolassa, Stephan}, + journal={Foresight: The International Journal of Applied Forecasting}, + year={2019}, + volume={52}, + pages={36-43} +} +``` +A two-part article introducing deep learning for forecasting. +``` +@article{januschowski18deeplearning2, +title = {Deep Learning for Forecasting: Current Trends and Challenges}, +journal = {Foresight: The International Journal of Applied Forecasting}, +year = "2018", +author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent}, +volume = {51}, +pages = {42-47} +} +``` +``` +@article{januschowski18deeplearning, + title = {Deep Learning for Forecasting}, + author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent}, + journal = {Foresight}, + year = {2018} +} +``` + +### System Aspects +A large-scale retail forecasting system. +``` +@article{bose2017probabilistic, + Author = {B{\"o}se, Joos-Hendrik and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Lange, Dustin and Salinas, David and Schelter, Sebastian and Seeger, Matthias and Wang, Yuyang}, + Date-Added = {2019-06-27 14:12:57 +0000}, + Date-Modified = {2019-06-27 14:13:35 +0000}, + Journal = {Proceedings of the VLDB Endowment}, + Number = {12}, + Pages = {1694--1705}, + Title = {Probabilistic demand forecasting at scale}, + Volume = {10}, + Year = {2017} +} +``` \ No newline at end of file From 1c66e165396fb488e128ec73f056e45eb9695e3b Mon Sep 17 00:00:00 2001 From: Tim Januschowski Date: Mon, 14 Oct 2019 13:47:38 +0200 Subject: [PATCH 3/5] fix headings hierachy --- REFERENCES.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/REFERENCES.md b/REFERENCES.md index 370bb69811..088c0dc393 100644 --- a/REFERENCES.md +++ b/REFERENCES.md @@ -3,7 +3,7 @@ We encourage you to also check out work by the group behind GluonTS. They are grouped according to topic and ordered chronographically. -### Methods +## Methods A number of the below methods are available in GluonTS. ``` @@ -69,7 +69,7 @@ A scalable state space model. -### Tutorials +## Tutorials Tutorials are available in bibtex and with accompanying material, in particular slides, linked from below. [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) @@ -114,7 +114,7 @@ Tutorials are available in bibtex and with accompanying material, } ``` -### General audience +## General audience An overview of forecasting libraries in Python. ``` @article{januschowski19opensource, @@ -166,7 +166,7 @@ pages = {42-47} } ``` -### System Aspects +## System Aspects A large-scale retail forecasting system. ``` @article{bose2017probabilistic, From c4cb7bcb80522029aa37990ce50d6b1544f0aaeb Mon Sep 17 00:00:00 2001 From: Tim Januschowski Date: Mon, 14 Oct 2019 13:52:12 +0200 Subject: [PATCH 4/5] added a comment on the REFERENCES --- REFERENCES.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/REFERENCES.md b/REFERENCES.md index 088c0dc393..5a366c9ea1 100644 --- a/REFERENCES.md +++ b/REFERENCES.md @@ -54,7 +54,8 @@ Using RNNs to parametrize State Space Models. Year = {2018} } ``` -A scalable state space model. +A scalable state space model. Note that code for this model +is currently not available in GluonTS. ``` @inproceedings{seeger2016bayesian, Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin}, From c17ed476d7e3a569a81f658c6219e7fee227c68d Mon Sep 17 00:00:00 2001 From: Tim Januschowski Date: Tue, 15 Oct 2019 18:37:14 +0200 Subject: [PATCH 5/5] including hyperlinks to the papers and clean-up bibtex --- REFERENCES.md | 69 +++++++++++++++++++++++++++------------------------ 1 file changed, 37 insertions(+), 32 deletions(-) diff --git a/REFERENCES.md b/REFERENCES.md index 5a366c9ea1..e7e7e7a50b 100644 --- a/REFERENCES.md +++ b/REFERENCES.md @@ -6,6 +6,7 @@ chronographically. ## Methods A number of the below methods are available in GluonTS. +[A multivariate forecasting model](https://arxiv.org/abs/1910.03002) ``` @inproceedings{salinas2019high, Author = {Salinas, David and Bohlke-Schneider, Michael and Callot, Laurent and Gasthaus, Jan}, @@ -15,9 +16,9 @@ A number of the below methods are available in GluonTS. } ``` -Deep Factor models, a global-local forecasting method. +[Deep Factor models, a global-local forecasting method.](http://proceedings.mlr.press/v97/wang19k.html) ``` -@inproceedings{wang2019deepfactors, +@inproceedings{wang2019deep, Author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim}, Booktitle = {International Conference on Machine Learning}, Pages = {6607--6617}, @@ -25,7 +26,7 @@ Deep Factor models, a global-local forecasting method. Year = {2019} } ``` -DeepAR, an RNN-based probabilistic forecasting model. +[DeepAR, an RNN-based probabilistic forecasting model](https://arxiv.org/abs/1704.04110) ``` @article{flunkert2019deepar, Author = {Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Tim Januschowski}, @@ -34,7 +35,7 @@ DeepAR, an RNN-based probabilistic forecasting model. Year = {2019} } ``` -A flexible way to model probabilistic forecasts via spline quantile forecasts. +[A flexible way to model probabilistic forecasts via spline quantile forecasts.](http://proceedings.mlr.press/v89/gasthaus19a.html) ``` @inproceedings{gasthaus2019probabilistic, Author = {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim}, @@ -44,7 +45,7 @@ A flexible way to model probabilistic forecasts via spline quantile forecasts. Year = {2019} } ``` -Using RNNs to parametrize State Space Models. +[Using RNNs to parametrize State Space Models.](https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting) ``` @inproceedings{rangapuram2018deep, Author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim}, @@ -54,14 +55,12 @@ Using RNNs to parametrize State Space Models. Year = {2018} } ``` -A scalable state space model. Note that code for this model -is currently not available in GluonTS. +[A scalable state space model. Note that code for this model +is currently not available in GluonTS.](https://papers.nips.cc/paper/6313-bayesian-intermittent-demand-forecasting-for-large-inventories) ``` @inproceedings{seeger2016bayesian, Author = {Seeger, Matthias W and Salinas, David and Flunkert, Valentin}, Booktitle = {Advances in Neural Information Processing Systems}, - Date-Added = {2019-06-27 13:17:25 +0000}, - Date-Modified = {2019-06-27 13:18:01 +0000}, Pages = {4646--4654}, Title = {Bayesian intermittent demand forecasting for large inventories}, Year = {2016} @@ -73,9 +72,12 @@ is currently not available in GluonTS. ## Tutorials Tutorials are available in bibtex and with accompanying material, in particular slides, linked from below. -[Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) + +### KDD 2019 +[paper](https://dl.acm.org/citation.cfm?id=3332289) +[slides](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) ``` -@inproceedings{faloutsos19forecasting2, +@inproceedings{faloutsos19forecasting, author = {Faloutsos, Christos and Flunkert, Valentin and Gasthaus, Jan and @@ -88,9 +90,11 @@ Tutorials are available in bibtex and with accompanying material, year = {2019} } ``` -[Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) +### SIGMOD 2019 +[paper](https://dl.acm.org/citation.cfm?id=3314033&dl=ACM&coll=DL) +[supporting material](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) ``` -@inproceedings{faloutsos2019forecasting, +@inproceedings{faloutsos2019classical, author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, title = {Classical and Contemporary Approaches to Big Time Series Forecasting}, booktitle = {Proceedings of the 2019 International Conference on Management of Data}, @@ -100,12 +104,12 @@ Tutorials are available in bibtex and with accompanying material, year = {2019} } ``` -[Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) +### VLDB 2018 +[paper](http://www.vldb.org/pvldb/vol11/p2102-faloutsos.pdf) +[supporting material](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) ``` @article{faloutsos2018forecasting, Author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, - Date-Added = {2019-07-24 13:47:16 +0000}, - Date-Modified = {2019-07-24 13:48:00 +0000}, Journal = {Proceedings of the VLDB Endowment}, Number = {12}, Pages = {2102--2105}, @@ -117,28 +121,29 @@ Tutorials are available in bibtex and with accompanying material, ## General audience An overview of forecasting libraries in Python. +[paper to appear](https://foresight.forecasters.org/wp-content/uploads/Foresight_Issue55_cumTOC.pdf) ``` -@article{januschowski19opensource, +@article{januschowski19open, title={Open-Source Forecasting Tools in Python}, author={Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang}, journal={Foresight: The International Journal of Applied Forecasting}, year={2019} } ``` -A commentary on the M4 competition and its classification of the participating methods -into 'statistical' and 'ML' methods. The article proposes alternative criteria. +[A commentary on the M4 competition and its classification of the participating methods +into 'statistical' and 'ML' methods. The article proposes alternative criteria.](https://www.sciencedirect.com/science/article/pii/S0169207019301529) ``` @article{januschowski19criteria, -title = "Criteria for classifying forecasting methods", -author = "Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent" -journal = "International Journal of Forecasting", -year = "2019" +title = {Criteria for classifying forecasting methods}, +author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Salinas, David and Flunkert, Valentin and Bohlke-Schneider, Michael and Callot, Laurent}, +journal = {International Journal of Forecasting}, +year = {2019} } ``` -The business forecasting problem landscape can be divided into -strategic, tactical and operational forecasting problems. +[The business forecasting problem landscape can be divided into +strategic, tactical and operational forecasting problems.](https://foresight.forecasters.org/product/foresight-issue-53/) ``` -@article{januschowski18classification, +@article{januschowski18a, title={A Classification of Business Forecasting Problems}, author={Januschowski, Tim and Kolassa, Stephan}, journal={Foresight: The International Journal of Applied Forecasting}, @@ -148,18 +153,20 @@ strategic, tactical and operational forecasting problems. } ``` A two-part article introducing deep learning for forecasting. +[part 2](https://foresight.forecasters.org/product/foresight-issue-52/) +[part 1](https://foresight.forecasters.org/product/foresight-issue-51/) ``` -@article{januschowski18deeplearning2, +@article{januschowski18deep2, title = {Deep Learning for Forecasting: Current Trends and Challenges}, journal = {Foresight: The International Journal of Applied Forecasting}, -year = "2018", +year = {2018}, author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent}, volume = {51}, pages = {42-47} } ``` ``` -@article{januschowski18deeplearning, +@article{januschowski18deep, title = {Deep Learning for Forecasting}, author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent}, journal = {Foresight}, @@ -168,12 +175,10 @@ pages = {42-47} ``` ## System Aspects -A large-scale retail forecasting system. +[A large-scale retail forecasting system.](http://www.vldb.org/pvldb/vol10/p1694-schelter.pdf) ``` @article{bose2017probabilistic, Author = {B{\"o}se, Joos-Hendrik and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Lange, Dustin and Salinas, David and Schelter, Sebastian and Seeger, Matthias and Wang, Yuyang}, - Date-Added = {2019-06-27 14:12:57 +0000}, - Date-Modified = {2019-06-27 14:13:35 +0000}, Journal = {Proceedings of the VLDB Endowment}, Number = {12}, Pages = {1694--1705},