AAAI'25 Workshop




AI4TS: AI for Time Series


Analysis:




Theory, Algorithms, and Applications


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 assive 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, key technical issues, and present solutions to tackle related challenges in practical applications. The workshop will focus on both the 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, econometrics, and 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: ai4ts.ijcai@gmail.com

Submissions should be 4-7 pages long, excluding references, and follow the AAAI2025 template. Submissions are double-blind and author identity should not 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 listed on the website (non-archival/without proceedings). A small number of accepted papers will be selected to be presented as contributed talks (15-minute oral presentations). We also welcome submissions of unpublished papers, including those submitted/accepted to other venues if that other venue allows.

Submission link: https://cmt3.research.microsoft.com/AI4TS2025

Note: Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

Key Dates

 

Workshop Paper Submission Due Date: December 1st, 2024 December 8th, 2024 (23:59pm AoE)

Notification of Paper Acceptance: December 15th, 2024 December 18th, 2024 (23:59pm AoE)

AAAI AI4TS Workshop Date: March 4th, 2025

Detailed Workshop Schedule:

TimeSpeakerTitle
9:00 am - 9:05 am Opening Remarks
9:05 am - 9:45 am  Keynote Talk 1
9:45 am - 10:35 am Oral Paper 1-5
10:35 am – 10:50 am Coffee Break
10:50 am – 11:30 am Keynote Talk 2
11:30 am - 12:10 pm Keynote Talk 3
12:10 pm - 2:00 pm Lunch Break
2:00 pm - 2: 40 pm  Keynote Talk 4
2:40 pm - 3: 20 pm  Keynote Talk 5
3:20 pm - 3:30 pm Award Ceremony
3:30 pm - 3:50 pm Coffee Break
3:50 pm - 4:30 pm Panel Discussion
4:30 pm - 6:00 pm Poster Session
 

Organizers

 

Dongjin Song

Assistant Professor, University of Connecticut

 

Qingsong Wen

Head of AI Research & Chief Scientist
Squirrel AI Learning

 

Yao Xie

Professor
Georgia Institute of Technology

 
 

Sanjay Purushotham

Assistant professor
University of Maryland Baltimore County

 

Haifeng Chen

Head
Data Science and System Security Department at NEC Laboratories America

 

Cong Shen

Assistant Professor
University of Virginia

 
 

Shirui Pan

Professor
Griffith University

 

Stefan Zohren

Associate Professor
University of Oxford

 

Yuriy Nevmyvaka

Managing Director
Machine Learning Research at Morgan Stanley