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add more references in README for related scientific papers (awslabs#389
) * add more references in README for related scientific papers * address comments by jaheba * fix headings hierachy * added a comment on the REFERENCES * including hyperlinks to the papers and clean-up bibtex
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# Scientific Articles | ||
We encourage you to also check out work by the group behind | ||
GluonTS. They are grouped according to topic and ordered | ||
chronographically. | ||
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||
## Methods | ||
A number of the below methods are available in GluonTS. | ||
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||
[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}, | ||
Booktitle = {Advances in Neural Information Processing Systems}, | ||
Title = {High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes}, | ||
Year = {2019} | ||
} | ||
``` | ||
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||
[Deep Factor models, a global-local forecasting method.](http://proceedings.mlr.press/v97/wang19k.html) | ||
``` | ||
@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}, | ||
Title = {Deep factors for forecasting}, | ||
Year = {2019} | ||
} | ||
``` | ||
[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}, | ||
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.](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}, | ||
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.](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}, | ||
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. 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}, | ||
Pages = {4646--4654}, | ||
Title = {Bayesian intermittent demand forecasting for large inventories}, | ||
Year = {2016} | ||
} | ||
``` | ||
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||
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||
## Tutorials | ||
Tutorials are available in bibtex and with accompanying material, | ||
in particular slides, linked from below. | ||
|
||
### KDD 2019 | ||
[paper](https://dl.acm.org/citation.cfm?id=3332289) | ||
[slides](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) | ||
``` | ||
@inproceedings{faloutsos19forecasting, | ||
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} | ||
} | ||
``` | ||
### 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{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}, | ||
series = {SIGMOD '19}, | ||
publisher = {ACM}, | ||
address = {New York, NY, USA}, | ||
year = {2019} | ||
} | ||
``` | ||
### 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}, | ||
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. | ||
[paper to appear](https://foresight.forecasters.org/wp-content/uploads/Foresight_Issue55_cumTOC.pdf) | ||
``` | ||
@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.](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} | ||
} | ||
``` | ||
[The business forecasting problem landscape can be divided into | ||
strategic, tactical and operational forecasting problems.](https://foresight.forecasters.org/product/foresight-issue-53/) | ||
``` | ||
@article{januschowski18a, | ||
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. | ||
[part 2](https://foresight.forecasters.org/product/foresight-issue-52/) | ||
[part 1](https://foresight.forecasters.org/product/foresight-issue-51/) | ||
``` | ||
@article{januschowski18deep2, | ||
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{januschowski18deep, | ||
title = {Deep Learning for Forecasting}, | ||
author = {Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama and Callot, Laurent}, | ||
journal = {Foresight}, | ||
year = {2018} | ||
} | ||
``` | ||
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||
## System Aspects | ||
[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}, | ||
Journal = {Proceedings of the VLDB Endowment}, | ||
Number = {12}, | ||
Pages = {1694--1705}, | ||
Title = {Probabilistic demand forecasting at scale}, | ||
Volume = {10}, | ||
Year = {2017} | ||
} | ||
``` |