Bayesian Intermittent Demand Forecasting for Large Inventories. - Matthias W. Seeger; David Salinas; Valentin Flunkert (NIPS, 2016)

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)

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)

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)

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)

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)

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)

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)

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)

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