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.

The top conferences including:

• Machine Learning: NeurIPS, ICML, ICLR
• Data Mining: KDD
• Artificial Intelligence: AAAI, IJCAI
• Data Management: SIGMOD, VLDB, ICDE
• Misc (selected): WWW, 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.

This list is also simultaneously updated in the Repo AI4TS Paper.

Most Recent Update Note

• [2022-06-02] Add papers accepted by ICML'22, ICLR'22, AAAI'22, IJCAI'22!

Table of Contents

• AI4TS Tutorials and Surveys

  • AI4TS Tutorials
  • AI4TS Surveys

• AI4TS Papers 2022

  • NeurIPS 2022, ICML 2022, ICLR 2022
  • KDD 2022, AAAI 2022, IJCAI 2022
  • SIGMOD VLDB ICDE 2022
  • Misc 2022

• AI4TS Papers 2021

  • NeurIPS 2021, ICML 2021, ICLR 2021
  • KDD 2021, AAAI 2021, IJCAI 2021
  • SIGMOD VLDB ICDE 2021
  • Misc 2021

• AI4TS Papers 201X-2020 Selected

  • NeurIPS 201X-2020, ICML 201X-2020, ICLR 201X-2020
  • KDD 201X-2020, AAAI 201X-2020, IJCAI 201X-2020
  • SIGMOD VLDB ICDE 201X-2020
  • Misc 201X-2020

AI4TS Tutorials and Surveys

AI4TS Tutorials

• Robust Time Series Analysis and Applications: An Industrial Perspective, in KDD 2022. [Link]
• Time Series in Healthcare: Challenges and Solutions, in AAAI 2022. [Link]
• Robust Time Series Analysis: from Theory to Applications in the AI Era, in IJCAI 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]
• 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. [Link] [Link2]

AI4TS Surveys

General Time Series Survey

• 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]
• A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in arXiv 2020. [paper]
• Transformers in Time Series: A Survey, in arXiv 2022. [paper]

Time Series Forecasting Survey

• Forecasting: theory and practice, in IJF 2022. [paper]
• Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
• Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions, in TITS 2022. [paper]
• Event prediction in the big data era: A systematic survey, in CSUR 2022. [paper]
• A brief history of forecasting competitions, in IJF 2020. [paper]
• Neural forecasting: Introduction and literature overview, in arXiv 2020. [paper]

Time Series Anomaly Detection Survey

• A review on outlier/anomaly detection in time series data, in CSUR 2021. [paper]
• Anomaly detection for IoT time-series data: A survey, in IEEE Internet of Things Journal 2019. [paper]
• A Survey of AIOps Methods for Failure Management, in TIST 2021. [paper]
• Sequential (quickest) change detection: Classical results and new directions, in IEEE Journal on Selected Areas in Information Theory 2021. [paper]
• Anomaly detection for discrete sequences: A survey, TKDE'12. [paper]

Time Series Classification Survey

• Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
• Approaches and Applications of Early Classification of Time Series: A Review, in IEEE Transactions on Artificial Intelligence 2020. [paper]

AI4TS Papers 2022

NeurIPS 2022

Not yet announced

ICML 2022

Time Series Forecasting

• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [paper] [official code]
• TACTiS: Transformer-Attentional Copulas for Time Series [paper]
• Domain Adaptation for Time Series Forecasting via Attention Sharing [paper]
• Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

Time Series Anomaly Detection

• Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

Other Time Series Analysis

• Adaptive Conformal Predictions for Time Series [paper] [official code]
• Modeling Irregular Time Series with Continuous Recurrent Units [paper]
• Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion [paper]
• Reconstructing nonlinear dynamical systems from multi-modal time series [paper]
• Utilizing Expert Features for Contrastive Learning of Time-Series Representations
• Learning of Cluster-based Feature Importance for Electronic Health Record Time-series

ICLR 2022

Time Series Forecasting

• Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting [paper] [official code]
• DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting [[paper]](https://openrevi