Tim Januschowski

David Salinas

Lorenzo Stella

François-Xavier Aubet

Laurent Callot

ACM Comput. Surv., 2023

Causal Forecasting for Pricing.

[BibT_eX]

[DOI]

CoRR, 2023

Deep Non-Parametric Time Series Forecaster.

[BibT_eX]

[DOI]

CoRR, 2023

Deep Learning based Forecasting: a case study from the online fashion industry.

[BibT_eX]

[DOI]

CoRR, 2023

Coherent Probabilistic Forecasting of Temporal Hierarchies.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Criteria for Classifying Forecasting Methods.

[BibT_eX]

[DOI]

Laurent Callot

CoRR, 2022

Intrinsic Anomaly Detection for Multi-Variate Time Series.

[BibT_eX]

[DOI]

Nicolas Papernot

Valentin Flunkert

CoRR, 2022

Diverse Counterfactual Explanations for Anomaly Detection in Time Series.

[BibT_eX]

[DOI]

Déborah Sulem

Michele Donini

Muhammad Bilal Zafar

Francois-Xavier Aubet

Jan Gasthaus

Sanjiv Das

Krishnaram Kenthapadi

Cédric Archambeau

CoRR, 2022

Multivariate Time Series Forecasting with Latent Graph Inference.

[BibT_eX]

[DOI]

Victor Garcia Satorras

CoRR, 2022

Multi-Objective Model Selection for Time Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

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

PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series.

[BibT_eX]

[DOI]

Paul Jeha

Proceedings of the Tenth International Conference on Learning Representations, 2022

Multivariate Quantile Function Forecaster.

[BibT_eX]

[DOI]

Kelvin Kan

François-Xavier Aubet

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

2021

Meta-Forecasting by combining Global Deep Representations with Local Adaptation.

[BibT_eX]

[DOI]

CoRR, 2021

A Study of Joint Graph Inference and Forecasting.

[BibT_eX]

[DOI]

Daniel Zügner

François-Xavier Aubet

Victor Garcia Satorras

Stephan Günnemann

Jan Gasthaus

CoRR, 2021

Detecting Anomalous Event Sequences with Temporal Point Processes.

[BibT_eX]

[DOI]

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

Online false discovery rate control for anomaly detection in time series.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

Neural Flows: Efficient Alternative to Neural ODEs.

[BibT_eX]

[DOI]

Marin Bilos

Johanna Sommer

Stephan Günnemann

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

Deep Explicit Duration Switching Models for Time Series.

[BibT_eX]

[DOI]

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

Neural Temporal Point Processes: A Review.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series.

[BibT_eX]

[DOI]

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

2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

J. Mach. Learn. Res., 2020

Forecasting: theory and practice.

[BibT_eX]

[DOI]

CoRR, 2020

Intermittent Demand Forecasting with Renewal Processes.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Neural forecasting: Introduction and literature overview.

[BibT_eX]

[DOI]

CoRR, 2020

Forecasting Big Time Series: Theory and Practice.

[BibT_eX]

[DOI]

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications.

[BibT_eX]

[DOI]

Proceedings of the AIDB@VLDB 2020, 2020

Elastic Machine Learning Algorithms in Amazon SageMaker.

[BibT_eX]

[DOI]

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

Resilient Neural Forecasting Systems.

[BibT_eX]

[DOI]

Shubham Kapoor

Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

Normalizing Kalman Filters for Multivariate Time Series Analysis.

[BibT_eX]

[DOI]

Emmanuel de Bézenac

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.

[BibT_eX]

[DOI]

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

2019

Intermittent Demand Forecasting with Deep Renewal Processes.

[BibT_eX]

[DOI]

Ali Caner Türkmen

Yuyang Wang

CoRR, 2019

GluonTS: Probabilistic Time Series Models in Python.

[BibT_eX]

[DOI]

Alexander Alexandrov

CoRR, 2019

End-to-End Benchmarking of Deep Learning Platforms.

[BibT_eX]

[DOI]

Proceedings of the Performance Evaluation and Benchmarking for the Era of Cloud(s), 2019

Classical and Contemporary Approaches to Big Time Series Forecasting.

[BibT_eX]

[DOI]

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

Deep Factors for Forecasting.

[BibT_eX]

[DOI]

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

Probabilistic Forecasting with Spline Quantile Function RNNs.

[BibT_eX]

[DOI]

Jan Gasthaus

Yuyang Wang

David Salinas

Valentin Flunkert

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

2018

Forecasting Big Time Series: Old and New.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2018

On Challenges in Machine Learning Model Management.

[BibT_eX]

[DOI]

IEEE Data Eng. Bull., 2018

Deep State Space Models for Time Series Forecasting.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2017

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

[BibT_eX]

[DOI]

Matthias W. Seeger

CoRR, 2017

2013

RTP: robust tenant placement for elastic in-memory database clusters.

[BibT_eX]

[DOI]

Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013

Realistic tenant traces for enterprise DBaaS.

[BibT_eX]

[DOI]

Jan Schaffner

Proceedings of the Workshops Proceedings of the 29th IEEE International Conference on Data Engineering, 2013

2011

The maximum k-colorable subgraph problem and orbitopes.

[BibT_eX]

[DOI]

Marc E. Pfetsch

Discret. Optim., 2011

Branch-Cut-and-Propagate for the Maximum <i>k</i>-Colorable Subgraph Problem with Symmetry.

[BibT_eX]

[DOI]

Marc E. Pfetsch

Proceedings of the Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2011

2010

Symmetry Breaking with Polynomial Delay

[BibT_eX]

[DOI]