基于机器学习/深度学习的时间序列分析相关论文

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)

擅长现代信号处理(改进小波分析系列，改进变分模态分解，改进经验小波变换，改进辛几何模态分解等等)，改进机器学习，改进深度学习，机械故障诊断，改进时间序列分析(金融信号，心电信号，振动信号等)

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