Awesome AI For Time Series Papers
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
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AI for Time Series (AI4TS) Papers, Tutorials, and Surveys
A professionally curated list of papers (with available code), tutorials, and surveys on recent AI for Time Series Analysis (AI4TS), including Time Series, Spatio-Temporal Data, Event Data, Sequence Data, Temporal Point Processes, etc., at the Top AI Conferences and Journals, which is updated ASAP (the earliest time) once the accepted papers are announced in the corresponding top AI conferences/journals. Hope this list would be helpful for researchers and engineers who are interested in AI for Time Series Analysis.
The top conferences including:
- Machine Learning: NeurIPS, ICML, ICLR
- Data Mining: KDD, WWW
- Artificial Intelligence: AAAI, IJCAI
- Data Management: SIGMOD, VLDB, ICDE
- Misc (selected): AISTAT, CIKM, ICDM, WSDM, SIGIR, ICASSP, CVPR, ICCV, etc.
The top journals including (mainly for survey papers): CACM, PIEEE, TPAMI, TKDE, TNNLS, TITS, TIST, SPM, JMLR, JAIR, CSUR, DMKD, KAIS, IJF, arXiv(selected), etc.
If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
For general Recent AI Advances: Tutorials and Surveys in various areas (DL, ML, DM, CV, NLP, Speech, etc.) at the Top AI Conferences and Journals, please check This Repo.
Main Recent Update Note
- [Mar. 04, 2024] Add papers accepted by ICLR'24, AAAI'24, WWW'24!
- [Jul. 05, 2023] Add papers accepted by KDD'23!
- [Jun. 20, 2023] Add papers accepted by ICML'23!
- [Feb. 07, 2023] Add papers accepted by ICLR'23 and AAAI'23!
- [Sep. 18, 2022] Add papers accepted by NeurIPS'22!
- [Jul. 14, 2022] Add papers accepted by KDD'22!
- [Jun. 02, 2022] Add papers accepted by ICML'22, ICLR'22, AAAI'22, IJCAI'22!
Table of Contents
AI4TS Tutorials and Surveys
AI4TS Tutorials
- Out-of-Distribution Generalization in Time Series, in AAAI 2024. [Link]
- Robust Time Series Analysis and Applications: An Interdisciplinary Approach, in ICDM 2023. [Link]
- Robust Time Series Analysis and Applications: An Industrial Perspective, in KDD 2022. [Link]
- Time Series in Healthcare: Challenges and Solutions, in AAAI 2022. [Link]
- Time Series Anomaly Detection: Tools, Techniques and Tricks, in DASFAA 2022. [Link]
- Modern Aspects of Big Time Series Forecasting, in IJCAI 2021. [Link]
- Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications, in AAAI 2021. [Link]
- Physics-Guided AI for Large-Scale Spatiotemporal Data, in KDD 2021. [Link]
- Deep Learning for Anomaly Detection, in KDD & WSDM 2020. [Link1] [Link2] [Link3]
- Building Forecasting Solutions Using Open-Source and Azure Machine Learning, in KDD 2020. [Link]
- Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data, KDD 2020. [Link]
- Forecasting Big Time Series: Theory and Practice, KDD 2019. [Link]
- Spatio-Temporal Event Forecasting and Precursor Identification, KDD 2019. [Link]
- Modeling and Applications for Temporal Point Processes, KDD 2019. [Link1] [Link2]
AI4TS Surveys
General Time Series Survey
- What Can Large Language Models Tell Us about Time Series Analysis, in arXiv 2024. [paper]
- Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook, in arXiv 2023. [paper] [Website]
- Deep Learning for Multivariate Time Series Imputation: A Survey, in arXiv 2024. [paper] [Website]
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in arXiv 2023. [paper] [Website]
- Transformers in Time Series: A Survey, in IJCAI 2023. [paper] [GitHub Repo]
- Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
- Neural temporal point processes: a review, in IJCAI 2021. [paper]
- Causal inference for time series analysis: problems, methods and evaluation, in KAIS 2022. [paper]
- Survey and Evaluation of Causal Discovery Methods for Time Series, in JAIR 2022. [paper]
- Deep learning for spatio-temporal data mining: A survey, in TKDE 2020. [paper]
- Generative Adversarial Networks for Spatio-temporal Data: A Survey, in TIST 2022. [paper]
- Spatio-Temporal Data Mining: A Survey of Problems and Methods, in CSUR 2018. [paper]
- A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series, in NeurIPS Workshop 2020. [paper]
- Count Time-Series Analysis: A signal processing perspective, in SPM 2019. [paper]
- Wavelet transform application for/in non-stationary time-series analysis: a review, in Applied Sciences 2019. [paper]
- Granger Causality: A Review and Recent Advances, in Annual Review of Statistics and Its Application 2014. [paper]
- A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in arXiv 2020. [paper]
- Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, in arXiv 2022. [paper]
- A Survey on Time-Series Pre-Trained Models, in arXiv 2023. [paper] [link]
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputatio
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Audited on Aug 5, 2026