Talks & Surveys

[Tutorial/Keynote Talks]

• [TK3] [ICDM'23 Tutorial] Qingsong Wen, Linxiao Yang, Tian Zhou, Weiqi Chen, Bingqing Peng, Liang Sun, "Robust Time Series Analysis and Applications: An Interdisciplinary Approach" in the 23rd IEEE International Conference on Data Mining (ICDM 2023), Shanghai, China, Dec. 1-4, 2023.  (Acceptance Rate=4/21=19%) [Website] [Slides]

• [TK2] [KDD'22 Tutorial] Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun, "Robust Time Series Analysis and Applications: An Industrial Perspective," in the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2022), Washington DC, USA, Aug. 14-18, 2022 (70+ onsite audiences).  [Website] [Slides]

• [TK1] [CIKM'22 WS Keynote] Qingsong Wen, “Customized Transformers for Time Series Forecasting”, Invited Keynote @ CIKM'22 Workshop on Applied Machine Learning Methods for Time Series Forecasting, Atlanta, USA, Oct. 21st, 2022. [Website] [Slides]

[Invited Talks]

• [IT4] “Time-LLM: Time Series Forecasting by Reprogramming Large Language Models”, Invited Talk @ 2023 CCF international AIOps Challenge and 'AlOps in the Age of Large Language Models' Summit, Dec. 16, 2023.  [Slides] [Report]

• [IT3] “Intelligent Time Series Analysis and Application”, Invited Talk @ Shanghai Jiao Tong University (SJTU), Mar. 27th, 2023.

• [IT2] “Intelligent Time Series Analysis and Application”, Invited Talk @ LOGS, Dec. 10th, 2022. [Slides]

• [IT1] “Robust and Intelligent Time Series Analysis and Application”, Invited Talk @ Tsinghua University, Oct. 28th, 2022 (100+ onsite and online audiences). [Slides]

[Survey/Position/Magazine Papers] 

( PIEEE x1, TPAMI x1, IJCAI x2, arXiv x10, etc. )   

AI for Education: GenAI, LLM, Standards, etc.

• [IS'24] Tianlong Xu, Richard Tong, Jing Liang, Xing Fan, Xing Fan, Qingsong Wen*, "Foundation Models for Education: Promises and Prospects", IEEE Intelligent Systems, 2024. [arXiv]

• [arXiv'24] Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen*, "Large Language Models for Education: A Survey and Outlook," Mar. 2024. [arXiv]

• [arXiv'24] Richard Tong, Haoyang Li, Joleen Liang, Qingsong Wen, "Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions", Mar. 2024. [arXiv]

• [arXiv'24] Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang, Xing Fan, Haoyang Li, Jiliang Tang, Qingsong Wen*, "Bringing Generative AI to Adaptive Learning in Education," Feb. 2024. [arXiv]

AI for Time Series & Spatio-Temporal Data: Large Model, LLM, Transformer, GNN, SSL, Data Augmentation, Imputation, etc.

• [TPAMI'24] Kexin Zhang, Qingsong Wen*, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan, "Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (CCF-A, IF=23.6) [arXiv] [paper] [Website] 

  • Note: The first work comprehensively and systematically summarizes Self-Supervised Learning for Time Series (SSL4TS).

• [arXiv'24] Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang*, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen*, "A Survey on Diffusion Models for Time Series and Spatio-Temporal Data," 2024. [arXiv] [Website]

• [arXiv'24] Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei Li, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Xiaofang Zhou, Yu Zheng, "Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond," 2024 [arXiv] [Website]

• [arXiv'24] Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen*, "Foundation Models for Time Series Analysis: A Tutorial and Survey," Mar. 2024. [arXiv]

• [arXiv'24] Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei Li, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Xiaofang Zhou, Yu Zheng, "Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond," Mar. 2024. [arXiv] [Website]

• [arXiv'24] Jun Wang, Wenjie Du, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen, "Deep Learning for Multivariate Time Series Imputation: A Survey," Feb. 2024. [arXiv] [Website] 

• [arXiv'24] Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen*, "Position Paper: What Can Large Language Models Tell Us about Time Series Analysis," Feb. 2024. [arXiv] 

• [arXiv'23] Ming Jin, Qingsong Wen*, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li (IEEE Fellow), Shirui Pan*, Vincent S. Tseng (IEEE Fellow), Yu Zheng (IEEE Fellow), Lei Chen (IEEE Fellow), Hui Xiong (IEEE Fellow), "Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook," Oct. 2023 [arXiv] [Website] 

  • Note: The first work comprehensively and systematically summarizes Large Models (LLM & PFM) for Time Series & Spatio-Temporal Data (LM4TS & LM4STD).

• [arXiv'23] Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi (IEEE Fellow), Geoffrey I. Webb (IEEE Fellow), Shirui Pan, "A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection," Jul. 2023 [arXiv] [Website] 

  • Note: The first work comprehensively and systematically summarizes Graph Neural Networks for Time Series  (GNN4TS).

• [AI Magazine'23] Zhaoyang Zhu, Weiqi Chen, Rui Xia, Tian Zhou, Peisong Niu, Bingqing Peng, Wenwei Wang, Hengbo Liu, Ziqing Ma, Xinyue Gu, Jin Wang, Qiming Chen, Linxiao Yang, Qingsong Wen, Liang Sun, “Energy Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms,” AAAI AI Magazine, 2023. [paper]

• [IJCAI'23] Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun, "Transformers in Time Series: A Survey," in the 32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, S.A.R, Aug. 2023. (CCF-A) [arXiv] [Website] 

  • Note: The first work comprehensively and systematically summarizes the Transformer Models for Time Series (Trans4TS).

  • Selected by Paper Digest into Most Influential IJCAI Papers (Version: 2023-09), Rank 1st (1/700+ IJCAI’23 papers).

• [IJCAI'21]  Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, Huan Xu, "Time Series Data Augmentation for Deep Learning: A Survey," in the 30th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada, Aug. 2021. (CCF-A) [paper] [arXiv] [Oral]. 

  • Note: The first work to comprehensively and systematically summarize Data Augmentation for Time Series (DataAug4TS); 

  • Selected by Paper Digest into Most Influential IJCAI Papers (Version: 2022-02) & (Version: 2022-05), Rank 1st (1/600+ IJCAI’21 papers).

Others: Energy Industry, Signal Processing, etc.

• [AAS'23] Qiming Chen,  Qingsong Wen,  Xun Lang, Lei  Xie,  Hongye Su, "Univariate and Multivariate Signal Decomposition: Review and Future Directions", in Acta Automatica Sinica (AAS), 2023. (CCF-A) [paper]

• [PIEEE'23] Yi Wang, Chien-fei Chen, Peng-Yong Kong, Husheng Li, and Qingsong Wen, “A Cyber-Physical-Social Perspective on Future Smart Distribution Systems,” Proceedings of the IEEE (PIEEE), 2023. (CCF-A, IF=20.6) [paper]