Robust Time Series Analysis and Applications: An Industrial Perspective, in?KDD?2022.
Time Series in Healthcare: Challenges and Solutions, in?AAAI?2022. [[Link]](https://www.vanderschaar-lab.com/time-series-in-healthcare/)
Time Series Anomaly Detection: Tools, Techniques and Tricks, in?DASFAA?2022. [[Link]](https://www.dasfaa2022.org//tutorials/Time%20Series%20Anomaly%20Result%20Master%20File_Dasfaa_2022.pdf)
Modern Aspects of Big Time Series Forecasting, in?IJCAI?2021
Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications, in?AAAI?2021.
Physics-Guided AI for Large-Scale Spatiotemporal Data, in?KDD?2021. [[Link]](https://sites.google.com/view/kdd2021tutorial/home)
Deep Learning for Anomaly Detection, in?KDD & WSDM?2020. [[Link1]](https://sites.google.com/view/kdd2020deepeye/home)
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]](https://xai.kaist.ac.kr/Tutorial/2020/)
Forecasting Big Time Series: Theory and Practice,?KDD?2019.
Spatio-Temporal Event Forecasting and Precursor Identification,?KDD?2019. [[Link]](http://mason.gmu.edu/~lzhao9/projects/event_forecasting_tutorial_KDD)
Modeling and Applications for Temporal Point Processes,?KDD?2019. [[Link1]](https://dl.acm.org/doi/10.1145/3292500.3332298)
General Time Series Survey
Transformers in Time Series: A Survey, in?IJCAI?2023. [[paper]](https://arxiv.org/abs/2202.07125)
Time series data augmentation for deep learning: a survey, in?IJCAI?2021. [[paper]](https://arxiv.org/abs/2002.12478)
Neural temporal point processes: a review, in?IJCAI?2021. [[paper]](https://arxiv.org/abs/2104.03528)
Causal inference for time series analysis: problems, methods and evaluation, in?KAIS?2022. [[paper]](https://scholar.google.com/scholar?cluster=15831734748668637115&hl=en&as_sdt=5,48&sciodt=0,48)
Survey and Evaluation of Causal Discovery Methods for Time Series, in?JAIR?2022. [[paper]](https://www.jair.org/index.php/jair/article/view/13428/26775)
Deep learning for spatio-temporal data mining: A survey, in?TKDE?2020. [[paper]](https://arxiv.org/abs/1906.04928)
Generative Adversarial Networks for Spatio-temporal Data: A Survey, in?TIST?2022. [[paper]](https://arxiv.org/abs/2008.08903)
Spatio-Temporal Data Mining: A Survey of Problems and Methods, in?CSUR?2018. [[paper]](https://dl.acm.org/doi/10.1145/3161602)
Modeling and Applications for Temporal Point Processes,?KDD?2019. [[Link1]](https://dl.acm.org/doi/10.1145/3292500.3332298)
General Time Series Survey
Transformers in Time Series: A Survey, in?IJCAI?2023. [[paper]](https://arxiv.org/abs/2202.07125)
Time series data augmentation for deep learning: a survey, in?IJCAI?2021. [[paper]](https://arxiv.org/abs/2002.12478)
Neural temporal point processes: a review, in?IJCAI?2021. [[paper]](https://arxiv.org/abs/2104.03528)
Causal inference for time series analysis: problems, methods and evaluation, in?KAIS?2022. [[paper]](https://scholar.google.com/scholar?cluster=15831734748668637115&hl=en&as_sdt=5,48&sciodt=0,48)
Survey and Evaluation of Causal Discovery Methods for Time Series, in?JAIR?2022. [[paper]](https://www.jair.org/index.php/jair/article/view/13428/26775)
Deep learning for spatio-temporal data mining: A survey, in?TKDE?2020. [[paper]](https://arxiv.org/abs/1906.04928)
Generative Adversarial Networks for Spatio-temporal Data: A Survey, in?TIST?2022. [[paper]](https://arxiv.org/abs/2008.08903)
Spatio-Temporal Data Mining: A Survey of Problems and Methods, in?CSUR?2018. [[paper]](https://dl.acm.org/doi/10.1145/3161602)
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series, in?NeurIPS Workshop?2020. [[paper]](https://arxiv.org/abs/2012.00168)
Count Time-Series Analysis: A signal processing perspective, in?SPM?2019. [[paper]](https://ieeexplore.ieee.org/document/8700675)
Wavelet transform application for/in non-stationary time-series analysis: a review, in?Applied Sciences?2019. [[paper]](https://www.mdpi.com/2076-3417/9/7/1345)
Granger Causality: A Review and Recent Advances, in?Annual Review of Statistics and Its Application?2014. [[paper]](https://www.annualreviews.org/doi/epdf/10.1146/annurev-statistics-040120-010930)
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in?arXiv?2020. [[paper]](https://arxiv.org/abs/2010.12493)
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, in?arXiv?2020. [[paper]](https://arxiv.org/abs/2206.02353)
Time Series Forecasting Survey
Forecasting: theory and practice, in?IJF?2022. [[paper]](https://www.sciencedirect.com/science/article/pii/S0169207021001758)
Time-series forecasting with deep learning: a survey, in?Philosophical Transactions of the Royal Society A?2021. [[paper]](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0209)
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions, in?TITS?2022. [[paper]](https://arxiv.org/abs/2004.08555)
Event prediction in the big data era: A systematic survey, in?CSUR?2022. [[paper]](https://dl.acm.org/doi/10.1145/3450287)
A brief history of forecasting competitions, in?IJF?2020. [[paper]](https://www.monash.edu/business/ebs/our-research/publications/ebs/wp03-2019.pdf)
Neural forecasting: Introduction and literature overview, in?arXiv?2020. [[paper]](https://arxiv.org/abs/2004.10240)
Probabilistic forecasting, in?Annual Review of Statistics and Its Application?2014. [[paper]](https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-062713-085831)
Time Series Anomaly Detection Survey
A review on outlier/anomaly detection in time series data, in?CSUR?2021. [[paper]](https://arxiv.org/abs/2002.04236)
Anomaly detection for IoT time-series data: A survey, in?IEEE Internet of Things Journal?2019. [[paper]](https://eprints.keele.ac.uk/7576/1/08926446.pdf)
A Survey of AIOps Methods for Failure Management, in?TIST?2021. [[paper]](https://jorge-cardoso.github.io/publications/Papers/JA-2021-025-Survey_AIOps_Methods_for_Failure_Management.pdf)
Sequential (quickest) change detection: Classical results and new directions, in?IEEE Journal on Selected Areas in Information Theory?2021. [[paper]](https://arxiv.org/abs/2104.04186)
Outlier detection for temporal data: A survey, TKDE'13. [[paper]](https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Gupta%20-%20Outlier%20Detection%20for%20Temporal%20Data%20-%202014.pdf)
Anomaly detection for discrete sequences: A survey, TKDE'12. [[paper]](https://ieeexplore.ieee.org/abstract/document/5645624)
Anomaly detection: A survey, CSUR'09. [[paper]](https://arindam.cs.illinois.edu/papers/09/anomaly.pdf)
Time Series Classification Survey
Deep learning for time series classification: a review, in?Data Mining and Knowledge Discovery?2019. [[paper]](https://link.springer.com/article/10.1007/s10618-019-00619-1?sap-outbound-id=11FC28E054C1A9EB6F54F987D4B526A6EE3495FD&mkt-key=005056A5C6311EE999A3A1E864CDA986)
Approaches and Applications of Early Classification of Time Series: A Review, in?IEEE Transactions on Artificial Intelligence?2020. [[paper]](https://arxiv.org/abs/2005.02595)
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