We encourage you to also check out the time series work by the group behind GluonTS, ordered chronographically.
- Learning Physical Models that Can Respect Conservation Laws, Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney, ICML 2023
- Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting, Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park, ICML 2023
- Guiding continuous operator learning through Physics-based boundary constraints, Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix, ICLR 2023
- Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms, Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan, ICLR 2023
- Coherent Probabilistic Forecasting of Temporal Hierarchies, Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Pedro Mercado, Yuyang Wang, Tim Januschowski, Michael Bohlke-Schneider, AISTATS 2023
- But are you sure? An uncertainty-aware perspective on explainable AI, Charlie Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Jun Huan, AISTATS 2023
- Domain Adaptation for Time Series Forecasting via Attention Sharing, Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang, ICML 2022
- Robust Probabilistic Time Series Forecasting, TaeHo Yoon, Youngsuk Park, Ernest Ryu, Yuyang Wang, AISTATS 2022
- Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting, Youngsuk Park, Danielle C. Maddix, Francois-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang, AISTATS 2022
- Multivariate Quantile Function Forecaster, Kelvin Kan , François-Xavier Aubet , Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus, AISTATS 2022
- Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection, Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot, AISTATS 2022
- Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies, Lenon Minorics, Caner Turkmen, Patrick Bloebaum, David Kernert, Laurent Callot, Dominik Janzing, 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
- Not All Domains Are Created Equal: Graph-Relational Domain Adaptation, Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang, ICLR 2022
- Forecasting with trees, Tim Januschowski, Yuyang Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson, Jan Gasthaus, IJF 2021
- Probabilistic Forecasting: A Level-Set Approach, Hilaf Hasson, Yuyang Wang, Tim Januschowski, and Jan Gasthaus, NeurIPS 2021.
- Deep Explicit Duration Switching Models for Time Series, Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alex Smola, Tim Januschowski, NeurIPS 2021
- Neural Flows: Efficient Alternative to Neural ODEs, Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann, NeurIPS 2021
- Detecting Anomalous Event Sequences with Temporal Point Processes, Oleksandr Shchur, Ali Caner Turkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann, NeurIPS 2021
- Online false discovery rate control for anomaly detection in time series, Quentin Rebjock, Baris Kurt, Tim Januschowski, Laurent Callot, NeurIPS 2021
- Symmetry-breaking for Variational Bayesian Neural Networks, Richard Kurle, Yuyang Wang, Tim Januschowski, Jan Gasthaus, NeurIPS 2021 Workshop on Bayesian Deep Learning
- GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics, Alex Wang, Danielle C. Maddix, Yuyang Wang, NeurIPS 2021 Workshop on ICBINB
- Modeling Advection on Directed Graphs using Graph Matern Gaussian Processes for Traffic Flow, Danielle C. Maddix, Nadim Saad, Yuyang Wang, NeurIPS 2021 Workshop on Machine Learning and The Physical Sciences
- Deep Generative model with Hierarchical Latent Factors for Timeseries Anomaly Detection, Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot, NeurIPS 2021 Workshop on Deep Generative Models
- Neural Temporal Point Processes: A Review, Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann, IJCAI 2021
- 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, ICML 2021 Workshop on Distribution-Free Uncertainty Quantification
- Revisiting Dynamic Regret of Strongly Adaptive Methods, Dheeraj Baby, Hilaf Hasson, Yuyang Wang, ICML Workshop on Time Series, 2021
- A Study of Joint Graph Inference and Forecasting, Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus, ICML Workshop on Time Series, 2021
- PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series, Jeha Paul, Bohlke-Schneider Michael, Mercado Pedro, Singh Nirwan Rajbir, Kapoor Shubham, Flunkert Valentin, Gasthaus Jan, Januschowski Tim, ICML Workshop on Time Series, 2021
- Variance Reduced Training with Stratified Sampling for Forecasting Models, Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster, ICML 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, ICML 2021
- Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems, Ray Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu, L4DC 2021
- Forecasting with Trees, Tim Januschowski, Yuyang Wang, Kari Torkkola, Timo Erkkila, Hilaf Hasson, Jan Gasthaus, International Journal of Forecasting (IJF) 2021
- Forecasting: Theory and Practice, Fotios Petroupolos et al and Tim Januschowski, International Journal of Forecasting (IJF) 2021
- The M5 Competition: A Critial Appraisal, Tim Januschowski, Jan Gasthaus, Yuyang Wang, Foresight, 2021
- Forecasting of intermittent and sparse time series: a unified probabilistic framework via deep renewal processes, Caner Turkmen, Tim Januschowski, Yuyang Wang, Ali Taylan Cemgil, PlosOne, 2021
- Deep Rao-Blackwellised Particle Filters for Time Series Forecasting, Richard Kurle, Syama Rangapuram, Emmanuel de Bezenac, Stepuhan Günnemann, Jan Gasthaus, NeurIPS 2020
- Normalizing Kalman Filters for Multivariate Time Series Analysis, Emmanuel de B'{e}zenac, Syama S. Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski, NeurIPS 2020
- Physics-based vs. Data-driven: A Benchmark Study on COVID-19 Forecasting, Ray Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu, Best Paper Award, NeurIPS 2020 Machine Learning in Public Health (MLPH) Workshop
- Criteria for classifying forecasting methods, Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot, International Journal of Forecasting, 2020
- DeepAR: Probabilistic forecasting with autoregressive recurrent networks, David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, International Journal of Forecasting, 2020
- Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models, Fadhel Ayed, Lorenzo Stella, Tim Januschowski, Jan Gasthaus, International Conference on Service-Oriented Computing, 2020
- Forecasting Big Time Series: Theory and Practice, Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang, WWW 2020
- Resilient neural forecasting systems, Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski, DEEM 2020
- Elastic machine learning algorithms in amazon sagemaker, Edo Liberty et al., SIGMOD 2020
- High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes, David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus, NeurIPS 2019
- FastPoint: Scalable Deep Point Processes, Ali Caner Turkmen, Yuyang Wang, Alex Smola, Best Paper Award, ECML 2019
- Forecasting Big Time Series: Theory and Practice, Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang, KDD 2019
- Classical and Contemporary Approaches to Big Time Series Forecasting, Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang, SIGMOD 2019
- Deep factors for forecasting, Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski, ICML 2019
- Probabilistic Forecasting with Spline Quantile Function RNNs, Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski, AISTATS 2019
- Open-Source Forecasting Tools in Python, Tim Januschowski, Jan Gasthaus, Yuyang Wang, Foresight: The International Journal of Applied Forecasting, 2019
- Deep state space models for time series forecasting, Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski, NeurIPS 2018
- Forecasting big time series: old and new, Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang, VLDB 2018
- Deep Learning for Forecasting: Current Trends and Challenges, Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent, Foresight: The International Journal of Applied Forecasting, 2018
- Deep Learning for Forecasting, Januschowski, Tim and Gasthaus, Jan and Wang, Yuyang and Rangapuram, Syama Sundar and Callot, Laurent, Foresight: The International Journal of Applied Forecasting, 2018
- A Classification of Business Forecasting Problems, Januschowski, Tim and Kolassa, Stephan, Foresight: The International Journal of Applied Forecasting, 2018
- Probabilistic demand forecasting at scale, Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias Seeger, Yuyang Wang, VLDB 2017
- Bayesian intermittent demand forecasting for large inventories, Matthias W. Seeger, David Salinas, Valentin Flunkert, NeurIPS 2016