Papers and Talks from Our Team

A full list of the publications and talks from PyPOTS team. * indicates co-first authors.

[Talk] Wenjie Du. Learning from Partially Observed Time Series: Towards Reality-Centric AI4TS, Keynote at IJCAI'24 AI4TS (AI for Time Series Analysis) Workshop, Jeju, South Korea, August 5, 2024.
[Slides to be updated here]

[Preprint] Linglong Qian, Tao Wang, Jun Wang, Hugh Logan Ellis, Robin Mitra, Richard Dobson, Zina Ibrahim. How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation, arXiv preprint, abs/2407.08442, 2024.
[DOI Link] [PDF]

[Preprint] Wenjie Du*, Jun Wang*, Linglong Qian*, Yiyuan Yang*, Fanxing Liu, Zepu Wang, Zina Ibrahim, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, Wenjia Wang, Kaize Ding, Yuxuan Liang, B. Aditya Prakash, Qingsong Wen. TSI-Bench: Benchmarking Time Series Imputation, arXiv preprint, abs/2406.12747, 2024.
[DOI Link] [PDF] [Code]

[Paper] Linglong Qian, Zina Ibrahim, Wenjie Du, Yiyuan Yang, Richard JB Dobson. Unveiling the Secrets: How Masking Strategies Shape Time Series Imputation, IJCAI'24 AI4TS (AI for Time Series Analysis) Workshop, 2024.
[DOI Link] [PDF] [Code]

[Preprint] Jun Wang*, Wenjie Du*, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen. Deep Learning for Multivariate Time Series Imputation: A Survey, arXiv preprint, abs/2402.04059, 2024.
[DOI Link] [PDF] [Code]

[Paper] Wenjie Du. PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series, arXiv preprint, abs/2305.18811, 2023. (A short version appeared in the 9th SIGKDD International Workshop on Mining and Learning from Time Series (MiLeTS'23))
[DOI Link] [PDF] [Code]

[Paper] Wenjie Du, David Cote, Yan Liu. SAITS: Self-Attention-based Imputation for Time Series, Expert Systems with Applications, 219:119619, 2023.
[DOI Link] [PDF] [Code] 2022 IF-8.665/JCR-Q1/CAS-Q1

[Talk] Wenjie Du. Efficient and Effective Time Series Imputation: from RNN to Transformer, Virtual Presentation at Ciena Corp., Montreal, Canada, July 9, 2021.

[Paper] Wenjie Du, David Cote, Chris Barber, Yan Liu. Forecasting Loss of Signal in Optical Networks with Machine Learning, IEEE/OSA Journal of Optical Communications and Networking, vol. 13, no. 10, pp. E109-E121 (2021).
[DOI Link] [PDF] [Code] 2022 IF-4.508/JCR-Q1/CAS-Q1