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A Study of Joint Graph Inference and Forecasting


Sep 10, 2021
Daniel Z├╝gner, Fran├žois-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan G├╝nnemann, Jan Gasthaus

* Published at the ICML 2021 Time Series Workshop 

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Neural Contextual Anomaly Detection for Time Series


Jul 16, 2021
Chris U. Carmona, Fran├žois-Xavier Aubet, Valentin Flunkert, Jan Gasthaus

* Chris and Fran\c{c}ois-Xavier contributed equally 

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Detecting Anomalous Event Sequences with Temporal Point Processes


Jun 08, 2021
Oleksandr Shchur, Ali Caner T├╝rkmen, Tim Januschowski, Jan Gasthaus, Stephan G├╝nnemann


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


Jul 30, 2020
Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus


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The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models


May 20, 2020
Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus


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Neural forecasting: Introduction and literature overview


Apr 21, 2020
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski

* 66 pages, 5 figures 

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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes


Oct 24, 2019
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus


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GluonTS: Probabilistic Time Series Models in Python


Jun 14, 2019
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner T├╝rkmen, Yuyang Wang

* ICML Time Series Workshop 2019 

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Deep Factors for Forecasting


May 28, 2019
Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

* Proceedings of Machine Learning Research, Volume 97: International Conference on Machine Learning, 2019 
* http://proceedings.mlr.press/v97/wang19k/wang19k.pdf. arXiv admin note: substantial text overlap with arXiv:1812.00098 

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


Sep 22, 2017
Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert


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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks


Jul 05, 2017
Valentin Flunkert, David Salinas, Jan Gasthaus


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GP-select: Accelerating EM using adaptive subspace preselection


Jul 17, 2016
Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Joerg Luecke, Arthur Gretton


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