5th AI for Time Series Analysis: Theory, Algorithms, and Applications


SDM Workshop 2024, April 18

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.

Call for Papers

Contact with us: Contact Us

Submissions should be 5-9 pages long, excluding references, and follow SDM24 template. Submissions are single-blind and author identity will be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).

Accepted papers will be presented as posters during the workshop and list on the website (non-archival/without proceedings). Besides, a small number of accepted papers will be selected to be presented as contributed talks. We also welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so.

Submission link: https://cmt3.research.microsoft.com/AI4TSSDM2024

Any questions may be directed to the workshop e-mail address: Contact Us

Key Dates

 

Workshop Paper Submission Due Date: February 26, 2024(AoE) March 04, 2024(AoE)

Notification of Paper Acceptance:March 15, 2024 March 16, 2024

Camera-ready Papers Due: April 5, 2024

SDM-24 Workshops: April 18, 2024

Tentative Workshop Schedule:

Time: April 18th


Location: Sunflower

Time(CDT)TitleSpeaker
10:00 am - 10:15 amOpening Remarks and Poster Setup
10:15 am - 10:30 amDeep Hashing for Multivariate Time Series via vMF Likeihood Loss
10:30 am - 10:45 amFew-shot Time Series Classification via Sharpness Aware Minimization
10:45 am – 11:00 amEfficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
11:00 am – 11:15 amLCL-SAX: Deep Learning with Local SAX for Univariate Time Series Classification
11:15 pm – 11:55 amKeynote Talk: An Exploration of Temporal Behavior of Social BotsSpeaker: Dr. Mueen Abdullah
12:00 pm – 1:30 pmLunch Break
1:30 pm - 1:45 pmHierarchical Functional Brain Network Learning with fMRI BOLD Signals
1:45 pm – 2:00 pmDeep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
2:00 pm – 2:15 pmSymbol-Temporal Consistency: A Self-supervised Learning Framework for Time Series Classification Under Distribution Shift 
2:15 pm – 2:55 pmKeynote Talk: Energy Efficient Federated LearningSpeaker: Dr. Miao Pan
2:55 pm – 3:25 pmPoster Session
3:25 pm – 3:30 pmClosing Remarks
 

Short Biography of Organizers




Dr. Yifeng Gao

Assistant Professor
University of Texas at Rio Grande Valley
Contact: yifeng.gao@utrgv.edu

Yifeng Gao is an assistant professor in the Department of Computer Science at University of Texas Rio Grande Valley. In the broad research area of data mining and machine learning, he has strong expertise in various time series data mining tasks including motif discovery, anomaly detection, and classification. He has publications in premier conferences and journals in the data mining and machine learning research field, including ICDM, AAAI, SDM, PKDD, EDBT, DMKD, and KAIS. He has served as a PC member in major data mining and machine learning conferences such as SDM, ICDM, ECML-PKDD, KDD, AAAI, IJCAI, and ICLR.

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. Sanjay Purushotham

Assistant professor
University of Maryland Baltimore County
Contact: psanjay@umbc.edu

Sanjay Purushotham is an Assistant Professor at the University of Maryland Baltimore County. His research interests are in machine learning, computer vision, time series analysis and its applications to biomedical informatics, climate science, and multimedia data mining. His current research work focuses on developing responsible AI/ML approaches and systems for biomedical applications with a focus on interpretable and explainable AI (XAI) topics. He has been a PI, co-PI and/or a key senior investigator on NSF, NIH, NASA, DoD, NCI funded projects, and he has collaborated with interdisciplinary researchers, and produced more than 50 peer-reviewed publications. He has organized “KDD Workshop on Mining and Learning from Time Series” in the past 5 years.

Program Committee