We encourage you to also check out work by the group behind GluonTS. They are grouped according to topic and ordered chronographically.
GluonTS: Probabilistic and Neural Time Series Modeling in Python
@article{maddix2019,
Author = {Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, Davis Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T\" urkmen, Yuyang Wang},
Journal = {Journal of Machine Learning Research},
Title = {GluonTS: Probabilistic and Neural Time Series Modeling in Python},
Year = {2020},
Volume = {21},
Number = {116},
Pages = {1-6}
}
A number of the below methods are available in GluonTS.
@inproceedings{bezene2020nkf,
Author = {Emmanuel de B\'{e}zenac, Syama S. Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski},
Booktitle = {Advances in Neural Information Processing Systems},
Title = {Normalizing Kalman Filters for Multivariate Time Series Analysis},
Year = {2020}
}
A multivariate forecasting model
@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}
}
@inproceedings{kurle20,
Author = {Richard Kurle, Syama Rangapuram, Emmanuel de Bezenac, Stepuhan Günnemann, Jan Gasthaus},
Booktitle = {Advances in Neural Information Processing Systems},
Title = {Deep Rao-Blackwellised Particle Filters for Time Series Forecasting},
Year = {2019}
}
Deep Factor models, a global-local forecasting method
@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
@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}
}
Intermittent Demand Forecasting with Renewal Processes
@inproceedings{turkmen2020idf,
Author = {T\"{u}rkmen, Ali Caner and Januschowski, Tim and Wang, Yuyang and Cemgil, Ali Taylan},
Booktitle = {arxiv},
Title = {Intermittent Demand Forecasting with Renewal Processes},
Year = {2020}
}
Using categorical distributions in forecasting
@inproceedings{rabanser2020discrete,
Author = {Rabanser, Stephan and Januschowski, Tim and Salinas, David and Flunkert, Valentin and Gasthaus, Jan},
Booktitle = {KDD Workshop on Mining and Learning From Time Series},
Title = {The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models},
Year = {2020}
}
Distributional Time Series Models for Anomaly Detection
@inproceedings{ayed20anomaly,
Author = {Ayed, Fadhel and Stella, Lorenzo and Januschowski, Tim and Gasthaus, Jan},
Booktitle = {AIOPs},
Title = {Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models},
Year = {2020}
}
Physics-Based Time Series Models for Learning Dynamical Systems with Distribution Shifts
@inproceedings{wang2020,
Author = {Wang, Rui and Maddix, Danielle and Faloutsos, Christos and Wang, Yuyang and Yu, Rose},
Booktitle = {NeurIPS 2020 Machine Learning in Public Health (MLPH) Workshop},
Title = {Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems},
Year = {2020}
}
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},
Booktitle = {Advances in Neural Information Processing Systems},
Pages = {4646--4654},
Title = {Bayesian intermittent demand forecasting for large inventories},
Year = {2016}
}
Tutorials are available in bibtex and with accompanying material, in particular slides, linked from below.
@inproceedings{faloutsos2020forecasting,
author = {Faloutsos, Christos and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang},
title = {Forecasting Big Time Series: Theory and Practice},
year = {2020},
booktitle = {Companion Proceedings of the Web Conference 2020},
pages = {320–321},
series = {WWW '20}
}
@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}
}
@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}
}
@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}
}
An overview of forecasting libraries in Python. paper to appear
@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}
}
@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}
}
@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 part 1
@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}
}
Resilient neural forecasting system.
@article{bohlke2020resilient,
Author = {Bohlke-Schneider, Michael and Kapoor, Shubham and Januschowski, Tim},
Journal = {DEEM'20: Proeccdings of the Fourth International Workshop on Data Management for End-to-End Machine Learning},
Title = {Resilient Neural Forecasting Systems},
Year = {2020}
}
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},
Journal = {Proceedings of the VLDB Endowment},
Number = {12},
Pages = {1694--1705},
Title = {Probabilistic demand forecasting at scale},
Volume = {10},
Year = {2017}
}