Dependent Dirichlet Process Spike Sorting. - Jan Gasthaus; Frank D. Wood; Dilan Görür; Yee Whye Teh (NIPS, 2008)

A stochastic memoizer for sequence data. - Frank D. Wood; Cédric Archambeau; Jan Gasthaus; Lancelot James; Yee Whye Teh (ICML, 2009)

Lossless Compression Based on the Sequence Memoizer. - Jan Gasthaus; Frank D. Wood; Yee Whye Teh (DCC, 2010)

Improvements to the Sequence Memoizer. - Jan Gasthaus; Yee Whye Teh (NIPS, 2010)

The sequence memoizer. - Frank D. Wood; Jan Gasthaus; Cédric Archambeau; Lancelot James; Yee Whye Teh (Commun. ACM, 2011)

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. - Valentin Flunkert; David Salinas; Jan Gasthaus (CoRR, 2017)

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. - Matthias W. Seeger; Syama Sundar Rangapuram; Yuyang Wang 001; David Salinas; Jan Gasthaus; Tim Januschowski; Valentin Flunkert (CoRR, 2017)

GP-Select: Accelerating EM Using Adaptive Subspace Preselection. - Jacquelyn A. Shelton; Jan Gasthaus; Zhenwen Dai; Jörg Lücke; Arthur Gretton (Neural Comput., 2017)

Probabilistic Demand Forecasting at Scale. - Joos-Hendrik Boese; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Dustin Lange; David Salinas; Sebastian Schelter; Matthias W. Seeger; Bernie Wang 001 (Proc. VLDB Endow., 2017)

Deep State Space Models for Time Series Forecasting. - Syama Sundar Rangapuram; Matthias W. Seeger; Jan Gasthaus; Lorenzo Stella; Yuyang Wang 001; Tim Januschowski (NeurIPS, 2018)

Forecasting Big Time Series: Old and New. - Christos Faloutsos; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (Proc. VLDB Endow., 2018)

Probabilistic Forecasting with Spline Quantile Function RNNs. - Jan Gasthaus; Konstantinos Benidis; Yuyang Wang 001; Syama Sundar Rangapuram; David Salinas; Valentin Flunkert; Tim Januschowski (AISTATS, 2019)

Deep Factors for Forecasting. - Yuyang Wang 001; Alex Smola; Danielle C. Maddix; Jan Gasthaus; Dean P. Foster; Tim Januschowski (ICML, 2019)

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. - David Salinas; Michael Bohlke-Schneider; Laurent Callot; Roberto Medico; Jan Gasthaus (NeurIPS, 2019)

Classical and Contemporary Approaches to Big Time Series Forecasting. - Christos Faloutsos; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (SIGMOD Conference, 2019)

GluonTS: Probabilistic Time Series Models in Python. - Alexander Alexandrov 001; Konstantinos Benidis; Michael Bohlke-Schneider; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Danielle C. Maddix; Syama Sundar Rangapuram; David Salinas; Jasper Schulz; Lorenzo Stella; Ali Caner Türkmen; et al. (CoRR, 2019)

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models. - Fadhel Ayed; Lorenzo Stella; Tim Januschowski; Jan Gasthaus (ICSOC Workshops, 2020)

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. - Richard Kurle; Syama Sundar Rangapuram; Emmanuel de Bézenac; Stephan Günnemann; Jan Gasthaus (NeurIPS, 2020)

Elastic Machine Learning Algorithms in Amazon SageMaker. - Edo Liberty; Zohar S. Karnin; Bing Xiang; Laurence Rouesnel; Baris Coskun; Ramesh Nallapati; Julio Delgado; Amir Sadoughi; Yury Astashonok; Piali Das; Can Balioglu; Saswata Chakravarty; et al. (SIGMOD Conference, 2020)

Forecasting Big Time Series: Theory and Practice. - Christos Faloutsos; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Yuyang Wang 001 (WWW (Companion Volume), 2020)

Neural forecasting: Introduction and literature overview. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Bernie Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; Laurent Callot; Tim Januschowski (CoRR, 2020)

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. - Stephan Rabanser; Tim Januschowski; Valentin Flunkert; David Salinas; Jan Gasthaus (CoRR, 2020)

GluonTS: Probabilistic and Neural Time Series Modeling in Python. - Alexander Alexandrov 001; Konstantinos Benidis; Michael Bohlke-Schneider; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Danielle C. Maddix; Syama Sundar Rangapuram; David Salinas; Jasper Schulz; Lorenzo Stella; Ali Caner Türkmen; et al. (J. Mach. Learn. Res., 2020)

Hierarchical Bayesian nonparametric models for power-law sequences - Jan Gasthaus (2020)

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series. - Syama Sundar Rangapuram; Lucien D. Werner; Konstantinos Benidis; Pedro Mercado; Jan Gasthaus; Tim Januschowski (ICML, 2021)

Probabilistic Forecasting: A Level-Set Approach. - Hilaf Hasson; Bernie Wang 001; Tim Januschowski; Jan Gasthaus (NeurIPS, 2021)

Detecting Anomalous Event Sequences with Temporal Point Processes. - Oleksandr Shchur; Ali Caner Türkmen; Tim Januschowski; Jan Gasthaus; Stephan Günnemann (NeurIPS, 2021)

A Study of Joint Graph Inference and Forecasting. - Daniel Zügner; François-Xavier Aubet; Victor Garcia Satorras; Tim Januschowski; Stephan Günnemann; Jan Gasthaus (CoRR, 2021)

Monte Carlo EM for Deep Time Series Anomaly Detection. - François-Xavier Aubet; Daniel Zügner; Jan Gasthaus (CoRR, 2021)

Multivariate Quantile Function Forecaster. - Kelvin Kan; François-Xavier Aubet; Tim Januschowski; Youngsuk Park; Konstantinos Benidis; Lars Ruthotto; Jan Gasthaus (AISTATS, 2022)

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. - Youngsuk Park; Danielle C. Maddix; François-Xavier Aubet; Kelvin Kan; Jan Gasthaus; Yuyang Wang 001 (AISTATS, 2022)

PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series. - Paul Jeha; Michael Bohlke-Schneider; Pedro Mercado; Shubham Kapoor; Rajbir-Singh Nirwan; Valentin Flunkert; Jan Gasthaus; Tim Januschowski (ICLR, 2022)

Neural Contextual Anomaly Detection for Time Series. - Chris U. Carmona; François-Xavier Aubet; Valentin Flunkert; Jan Gasthaus (IJCAI, 2022)

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. - Sanjay Purushotham; Jun Huan; Cong Shen 001; Dongjin Song; Yuyang Wang 001; Jan Gasthaus; Hilaf Hasson; Youngsuk Park; Sungyong Seo; Yuriy Nevmyvaka (KDD, 2022)

On the detrimental effect of invariances in the likelihood for variational inference. - Richard Kurle; Ralf Herbrich; Tim Januschowski; Yuyang Wang 001; Jan Gasthaus (NeurIPS, 2022)

Online Time Series Anomaly Detection with State Space Gaussian Processes. - Christian Bock; François-Xavier Aubet; Jan Gasthaus; Andrey Kan; Ming Chen; Laurent Callot (CoRR, 2022)

Diverse Counterfactual Explanations for Anomaly Detection in Time Series. - Déborah Sulem; Michele Donini; Muhammad Bilal Zafar; Francois-Xavier Aubet; Jan Gasthaus; Tim Januschowski; Sanjiv Das; Krishnaram Kenthapadi; Cédric Archambeau (CoRR, 2022)

Intrinsic Anomaly Detection for Multi-Variate Time Series. - Stephan Rabanser; Tim Januschowski; Kashif Rasul; Oliver Borchert; Richard Kurle; Jan Gasthaus; Michael Bohlke-Schneider; Nicolas Papernot; Valentin Flunkert (CoRR, 2022)

Criteria for Classifying Forecasting Methods. - Tim Januschowski; Jan Gasthaus; Yuyang Wang 001; David Salinas; Valentin Flunkert; Michael Bohlke-Schneider; Laurent Callot (CoRR, 2022)

Deep Non-Parametric Time Series Forecaster. - Syama Sundar Rangapuram; Jan Gasthaus; Lorenzo Stella; Valentin Flunkert; David Salinas; Yuyang Wang 001; Tim Januschowski (CoRR, 2023)

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Yuyang Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; François-Xavier Aubet; Laurent Callot; et al. (ACM Comput. Surv., 2023)

A Flexible Forecasting Stack. - Tim Januschowski; Yuyang Wang 001; Jan Gasthaus; Syama Sundar Rangapuram; Caner Türkmen; Jasper Zschiegner; Lorenzo Stella; Michael Bohlke-Schneider; Danielle C. Maddix; Konstantinos Benidis; Alexander Alexandrov 001; Christos Faloutsos; et al. (Proc. VLDB Endow., 2024)