Jan Gasthaus

According to our database, Jan Gasthaus authored at least 41 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski

ACM Comput. Surv., 2023

Deep Non-Parametric Time Series Forecaster.

Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski

CoRR, 2023

2022

Criteria for Classifying Forecasting Methods.

Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot

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

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

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

On the detrimental effect of invariances in the likelihood for variational inference.

Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications.

Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Neural Contextual Anomaly Detection for Time Series.

Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, Jan Gasthaus

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 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

Proceedings of the Tenth International Conference on Learning Representations, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Multivariate Quantile Function Forecaster.

Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Monte Carlo EM for Deep Time Series Anomaly Detection.

François-Xavier Aubet, Daniel Zügner, Jan Gasthaus

CoRR, 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

Detecting Anomalous Event Sequences with Temporal Point Processes.

Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Probabilistic Forecasting: A Level-Set Approach.

Hilaf Hasson, Bernie Wang, Tim Januschowski, Jan Gasthaus

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Hierarchical Bayesian nonparametric models for power-law sequences

Jan Gasthaus

PhD thesis, 2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

Alexander Alexandrov, 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, Yuyang Wang

J. Mach. Learn. Res., 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

Neural forecasting: Introduction and literature overview.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski

CoRR, 2020

Forecasting Big Time Series: Theory and Practice.

Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang

Proceedings of the Companion of The 2020 Web Conference 2020, 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, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola

Proceedings of the 2020 International Conference on Management of Data, 2020

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting.

Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models.

Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus

Proceedings of the Service-Oriented Computing - ICSOC 2020 Workshops, 2020

2019

GluonTS: Probabilistic Time Series Models in Python.

Alexander Alexandrov, 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, Yuyang Wang

CoRR, 2019

Classical and Contemporary Approaches to Big Time Series Forecasting.

Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang

Proceedings of the 2019 International Conference on Management of Data, 2019

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes.

David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Factors for Forecasting.

Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

Proceedings of the 36th International Conference on Machine Learning, 2019

Probabilistic Forecasting with Spline Quantile Function RNNs.

Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Forecasting Big Time Series: Old and New.

Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang

Proc. VLDB Endow., 2018

Deep State Space Models for Time Series Forecasting.

Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Proc. VLDB Endow., 2017

GP-Select: Accelerating EM Using Adaptive Subspace Preselection.

Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton

Neural Comput., 2017

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale.

Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert

CoRR, 2017

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks.

Valentin Flunkert, David Salinas, Jan Gasthaus

CoRR, 2017

2011

The sequence memoizer.

Frank D. Wood, Jan Gasthaus, Cédric Archambeau, Lancelot James, Yee Whye Teh

Commun. ACM, 2011

2010

Improvements to the Sequence Memoizer.

Jan Gasthaus, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Lossless Compression Based on the Sequence Memoizer.

Jan Gasthaus, Frank D. Wood, Yee Whye Teh

Proceedings of the 2010 Data Compression Conference (DCC 2010), 2010

2009

A stochastic memoizer for sequence data.

Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Dependent Dirichlet Process Spike Sorting.

Jan Gasthaus, Frank D. Wood, Dilan Görür, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 21, 2008