Symmetry Breaking with Polynomial Delay - Tim Januschowski; Barbara M. Smith; Marc R. C. van Dongen (CoRR, 2010)

Branch-Cut-and-Propagate for the Maximum k-Colorable Subgraph Problem with Symmetry. - Tim Januschowski; Marc E. Pfetsch (CPAIOR, 2011)

The maximum k-colorable subgraph problem and orbitopes. - Tim Januschowski; Marc E. Pfetsch (Discret. Optim., 2011)

Realistic tenant traces for enterprise DBaaS. - Jan Schaffner; Tim Januschowski (ICDE Workshops, 2013)

RTP: robust tenant placement for elastic in-memory database clusters. - Jan Schaffner; Tim Januschowski; Megan Kercher; Tim Kraska; Hasso Plattner; Michael J. Franklin; Dean Jacobs (SIGMOD Conference, 2013)

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)

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)

On Challenges in Machine Learning Model Management. - Sebastian Schelter; Felix Bießmann; Tim Januschowski; David Salinas; Stephan Seufert; Gyuri Szarvas (IEEE Data Eng. Bull., 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)

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

End-to-End Benchmarking of Deep Learning Platforms. - Vincent Deuschle; Alexander Alexandrov 001; Tim Januschowski; Volker Markl (TPCTC, 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)

Intermittent Demand Forecasting with Deep Renewal Processes. - Ali Caner Türkmen; Yuyang Wang 001; Tim Januschowski (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)

Normalizing Kalman Filters for Multivariate Time Series Analysis. - Emmanuel de Bézenac; Syama Sundar Rangapuram; Konstantinos Benidis; Michael Bohlke-Schneider; Richard Kurle; Lorenzo Stella; Hilaf Hasson; Patrick Gallinari; Tim Januschowski (NeurIPS, 2020)

Resilient Neural Forecasting Systems. - Michael Bohlke-Schneider; Shubham Kapoor; Tim Januschowski (DEEM@SIGMOD, 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)

A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications. - Quentin Rebjock; Valentin Flunkert; Tim Januschowski; Laurent Callot; Joel Castellon (AIDB@VLDB, 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)

Intermittent Demand Forecasting with Renewal Processes. - Ali Caner Türkmen; Tim Januschowski; Yuyang Wang 001; Ali Taylan Cemgil (CoRR, 2020)

Forecasting: theory and practice. - Fotios Petropoulos; Daniele Apiletti; Vassilios Assimakopoulos; Mohamed Zied Babai; Devon K. Barrow; Souhaib Ben Taieb; Christoph Bergmeir; Ricardo J. Bessa; Jakub Bijak; John E. Boylan; Jethro Browell; Claudio Carnevale; et al. (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)

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)

Neural Temporal Point Processes: A Review. - Oleksandr Shchur; Ali Caner Türkmen; Tim Januschowski; Stephan Günnemann (IJCAI, 2021)

Deep Explicit Duration Switching Models for Time Series. - Abdul Fatir Ansari; Konstantinos Benidis; Richard Kurle; Ali Caner Türkmen; Harold Soh; Alexander J. Smola; Bernie Wang 001; Tim Januschowski (NeurIPS, 2021)

Neural Flows: Efficient Alternative to Neural ODEs. - Marin Bilos; Johanna Sommer; Syama Sundar Rangapuram; Tim Januschowski; Stephan Günnemann (NeurIPS, 2021)

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

Online false discovery rate control for anomaly detection in time series. - Quentin Rebjock; Baris Kurt; Tim Januschowski; Laurent Callot (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)

Meta-Forecasting by combining Global Deep Representations with Local Adaptation. - Riccardo Grazzi; Valentin Flunkert; David Salinas; Tim Januschowski; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

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

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)

Multi-Objective Model Selection for Time Series Forecasting. - Oliver Borchert; David Salinas; Valentin Flunkert; Tim Januschowski; Stephan Günnemann (CoRR, 2022)

Multivariate Time Series Forecasting with Latent Graph Inference. - Victor Garcia Satorras; Syama Sundar Rangapuram; Tim Januschowski (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)

Coherent Probabilistic Forecasting of Temporal Hierarchies. - Syama Sundar Rangapuram; Shubham Kapoor; Rajbir-Singh Nirwan; Pedro Mercado; Tim Januschowski; Yuyang Wang 001; Michael Bohlke-Schneider (AISTATS, 2023)

Deep Learning based Forecasting: a case study from the online fashion industry. - Manuel Kunz; Stefan Birr; Mones Raslan; Lei Ma; Zhen Li; Adèle Gouttes; Mateusz Koren; Tofigh Naghibi; Johannes Stephan; Mariia Bulycheva; Matthias Grzeschik; Armin Kekic; et al. (CoRR, 2023)

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

Causal Forecasting for Pricing. - Douglas Schultz; Johannes Stephan; Julian Sieber; Trudie Yeh; Manuel Kunz; Patrick Doupe; 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)

Unity Catalog: Open and Universal Governance for the Lakehouse and Beyond. - Ramesh Chandra; Haogang Chen 003; Ray Matharu; Sarah Cai; Jeff Chen; Priyam Dutta; Bogdan Ghita 004; Todd Greenstein; Gopal Holla; Peng Huang; Yuchen Huo; Adrian Ionescu; et al. (SIGMOD Conference Companion, 2025)