AI for Time Series Analysis: Theory, Algorithms, and Applications


SDM Workshop 2025 Submission

Li Zhang, Assistant Professor, University of Texas Rio Grande Valley
Dongjing Song, Assistant Professor, University of Connecticut
Qingsong Wen, Head of AI Research & Chief Scientist, Squirrel AI Learning
Shirui Pan, Professor and ARC Future Fellow, Griffith University

Time series data are becoming ubiquitous in numerous real-world applications, e.g., IoT devices, healthcare, wearable devices, smart vehicles, financial markets, biological sciences, environmental sciences, etc. Given the availability of massive amounts of data, their complex underlying structures/distributions, together with the high-performance computing platforms, there is a great demand for developing new theories and algorithms to tackle fundamental challenges (e.g., representation, classification, prediction, causal analysis, etc.) in various types of applications. The goal of this workshop is to provide a platform for researchers and AI practitioners from both academia and industry to discuss potential research directions, and key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will focus on both theoretical and practical aspects of time series data analysis and aims to trigger research innovations in theories, algorithms, and applications. We will invite researchers and AI practitioners from the related areas of machine learning, data science, statistics, astronomy, and urban design to many others to contribute to this workshop.

This workshop encourages submissions of innovative solutions for a broad range of time series analysis problems. Topics of interest include but are not limited to the following:

  • Time series forecasting and prediction
  • Spatio-temporal forecasting and prediction
  • Time series anomaly detection and diagnosis
  • Time series change point detection
  • Time series classification and clustering
  • Time series similarity search
  • Time series indexing
  • Time series compression
  • Time series pattern discovery
  • Interpretation and explanation in time series
  • Causal inference in time series
  • Bias and fairness in time series
  • Federated learning and security in time series
  • Benchmarks, experimental evaluation, and comparison for time series analysis tasks
  • Time series applications in various areas: E-commerce, Cloud computing, Transportation, Fintech, Healthcare, Internet of things, Wireless networks, Predictive maintenance, Energy, and Climate, etc.

Target Audience

We plan to organize prefer to organize full-day workshop but half-day in-person workshop is also an option. If it is a full day workshop, it will consist of keynote presentations, paper presentations, and a poster presentation. If it is a half-day in-person workshop, it will consist of mainly keynote presentations, selected oral presentation, and poster presentation.

We will invite 2-3 domain experts from both industry and academia. Each speaker will give a 45-minute talk about their recent work and insights on future directions with 5 minutes Q\&A. We plan to invite the following researchers that work on time series and spatio-temporal data mining to deliver the keynotes:


Dr. Jessica Lin, George Mason University
Dr. Jiahao Ding, Amazon
Dr. Mucun Sun, Idaho National Lab

Given the ubiquity of time series data, our target audience is very broad including researchers, graduate students, as well as industrial practitioners who are interested in time series data mining, database, and practical sensor-based applications such as health, meteorology, transportation, and manufacturing. Given SDM is well known to be one of the most prestigious venues for sharing time series data mining research, we are anticipating a wide range of researchers who attending SDM will participate the workshop.

Previous Workshop at SDM

 

Last year, the AI4TS workshop at SDM2024 has more than 20 attendees (meet the expectation number we proposed in last year workshop proposal) with a total of 9 submissions. We are anticipating an approximately similiar attendees numbers (20-30 attendees) will attend the proposed workshop.

Tentative Workshop Schedule:

Time: TBD


Location: TBD


Zoom Link: TBD

Time(GMT+8)TitleSpeaker
8:45 am - 9:00 amOpening Remarks
9:00 am - 9:30 amCoffee Break
9:30 am - 10:30 amKeynote Talk
10:30 am – 12:00 pmContributed oral talks
12:00pm – 1: 30 pmLunch Break
1:30 pm - 2:30 pmKeynote Talk  
2:30 pm – 3:30 pmPoster Session
3:30pm – 4:00pmCoffee Break 
4:15pm – 5:00pmPanel Discussion
5:00pm – 5:10pmClosing Remark
 

Short Biography of Organizers




Dr. Li Zhang

Assistant Professor
University of Texas at Rio Grande Valley
Contact: li.zhang@utrgv.edu

Li Zhang is an assistant professor in the Department of Computer Science at University of Texas Rio Grande Valley. Her research focuses on designing reliable time series data mining tools for various tasks including time series forecasting, pattern discovery, anomaly detection, classification. Her work have appeared on top-tier data mining conferences such as ICDM, SDM and CIKM, as well as interdisciplinary applications related to time series including one best student paper runner-up. She has hosted a tutorial on time series pattern mining in SDM24 and organized AI4TS time series workshop in IJCAI 2024. She has served as an associate editor for Big Data Research, and a PC member in major data mining and machine learning conferences such as KDD, SDM, ICDM, Neurips, AAAI, etc. Her research is sponsored by the National Science Foundation.

Dr. Dongjin Song

Assistant Professor
University of Connecticut
Contact: dongjin.song@uconn.edu

Dongjin Song is an assistant professor in the Department of Computer Science and Engineering at the University of Connecticut. He has strong expertise in machine learning, deep learning, and related applications for time series data analysis (including representation, prediction, anomaly detection, and diagnosis). Papers describing his research have been published at top-tier data science and artificial intelligence conferences, such as AAAI, IJCAI, NeurIPS, ICML, ICLR, KDD, ICCV, ICDM, SDM, etc. He has served as Senior PC for AAAI, IJCAI, and CIKM. He won the UConn Research Excellence Research (REP) Award in 2021.

Dr. Qingsong Wen

Head of AI Research & Chief Scientist
Squirrel AI Learning
Contact: qingsongedu@gmail.com

Qingsong Wen is currently the Head of AI Research & Chief Scientist at Squirrel AI Learning by Yixue Education Inc., working on AI for Education via SOTA technologies (like LLM, AI Agent, GenAI, Transformer, SSL, GNN, etc.). Previously, he worked at Alibaba on AI for Time Series in Cloud Computing, E-Commerce, and Energy Industries, at Qualcomm and Marvell in big data and signal processing. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, USA. He has published over 80 top-ranked conference and journal papers, received AAAI/IAAI 2023 Innovative Application Award, and won the First Place in 2022 ICASSP Grand Challenge Competition. He is an Associate Editor for Neurocomputing, Guest Editor for IEEE Internet of Things Journal, Guest Editor for Applied Energy, and regularly served as an Area Chiar/SPC/PC member of the major AI/DM/SP conferences including AAAI, IJCAI, KDD, ICDM, ICASSP, etc.

Dr. Shirui Pan

Professor and ARC Future Fellow
Griffith University
Contact: s.pan@griffith.edu.au

Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. Before joining Griffith in 2022, he is a Senior Member of IEEE and ACM, and a Fellow of Queensland Academy of Arts and Sciences (FQA). His research focuses on artificial intelligence and machine learning. He has made contributions to advance graph machine learning methods for solving hard AI problems for real-life applications, including graph classification, anomaly detection, recommender systems, and multivariate time series forecasting. His research has been published in top conferences and journals including NeurIPS, ICML, KDD, TPAMI, TNNLS, and TKDE. He is recognised as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2023, 2022), and one of the World’s Top 2% Scientists (2022, 2021). His research received the 2020 IEEE ICDM Best Student Paper Award (2020), and the 2024 IEEE CIS TNNLS Outstanding Paper Award. He has eight papers recognised as the Most Influential Papers in KDD, IJCAI, AAAI, and CIKM (x1). He received a prestigious Future Fellowship (2022-2025), one of the most competitive grants from the Australian Research Council (ARC).

Program Committee (Tentative)

Chen Xu, Georgia Institute of Technology
Defu Cao, University of Southern California
Dongsheng Luo, FIU
Haomin Wen, Beijing Jiaotong University
Wei Zhu, University of Rochester
Xiang Zhang, UNC Charlotte
Xikun Zhang, The University of Sydney
Yijun Tian, University of Notre Dame
Yushun Dong, Florida State University