CFP: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications @ AAAI 2025
Dongjin Song
CFP: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications @ AAAI 2025
Workshop website
https://ai4ts.github.io/aaai2025
Submission link:
https://cmt3.research.microsoft.com/AI4TS2025
Important Dates:
Submission Deadline: December 1 (23:59 pm AoE), 2024
Acceptance Notification: December 15 (23:59pm AoE), 2024
Workshop Date: March 4, 2025
Call for Paper:
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, 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, AI practitioners, and policymakers from the related areas of machine learning, data science, statistics, econometrics, and many others to contribute to this workshop.
Topics:
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
AI-inspired approaches for time series similarity search
Time series indexing
Time series compression
Time series pattern discovery
Time series pattern discovery
Interpretation and explanation in time series
Causal discovery and inference in time series
Bias and fairness in time series
Benchmarks, experimental evaluation, and comparison for time series analysis tasks
Foundation models and LLMs for time series analysis
Time series applications in various areas: E-commerce,
Cloud computing, Transportation, Fintech, Healthcare,
Internet of things, Wireless networks, Predictive maintenance, Education, Energy, Climate, etc.
Important Dates:
Submission Deadline: December 1 (23:59 pm AoE), 2024
Acceptance Notification: December 15 (23:59pm AoE), 2024
Workshop Date: March 4, 2025
Attendance:
Researchers, students, and practitioners in AI, machine learning, data mining, and time series analysis.
Submission Requirements:
Submissions should be 4-7 pages long, excluding references, and follow the AAAI 2025 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).
Submission link:
https://cmt3.research.microsoft.com/AI4TS2025
Workshop Organizers
Dongjin Song, University of Connecticut
Qingsong Wen, Squirrel Ai Learning
Yao Xie, Georgia Institute of Technology
Cong Shen, University of Virginia
Sanjay Purushotham, University of Maryland Baltimore County
Shirui Pan, Griffith University
Stefan Zohren, University of Oxford
Haifeng Chen, NEC Labs America, Princeton
Yuriy Nevmyvaka, Morgan Stanley