REFERENCES.md

Scientific Articles

We encourage you to also check out the time series work by the group behind GluonTS, ordered chronographically.

2023

• Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting, Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang Wang, NeurIPS 2023
• 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

2022

• 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

2021

• 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

2020

• 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

2019 and Earlier

• 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