Overview
The AAAI 2026 Workshop on AI for Time Series (AI4TS) is set to take place on January 26, 2026, in Singapore. This workshop serves as a significant platform for researchers and practitioners to delve into the intersection of artificial intelligence and time series analysis. It aims to foster discussions on innovative theories, algorithms, and applications pertinent to time series data.
Background & Relevance
Time series data is increasingly prevalent across various sectors, including healthcare, finance, and environmental science. With the rise of IoT devices and advanced computing capabilities, there is a pressing need to develop new methodologies to address challenges such as prediction, classification, and causal analysis. This workshop is vital for advancing research in these areas, providing insights into how AI can enhance the analysis of temporal data.
Key Details
- Workshop Date: January 26, 2026
- Location: Singapore, AAAI 2026
- Paper Submission Deadline: October 22, 2025 (23:59 AoE)
- Notification of Acceptance: November 5, 2025 (23:59 AoE)
- Submission Link: CMT Submission
- Workshop Website: AI4TS 2026
Eligibility & Participation
This workshop invites participation from a diverse audience, including researchers, students, and industry professionals. It targets individuals interested in the theoretical and practical aspects of time series data analysis, as well as those looking to contribute to advancements in AI methodologies.
Submission or Application Guidelines
Participants are encouraged to submit papers that explore various topics related to time series analysis. The submission process involves:
1. Preparing a paper that aligns with the workshop themes.
2. Submitting the paper through the provided CMT link before the deadline.
3. Awaiting notification of acceptance as per the timeline outlined.
Additional Context / Real-World Relevance
The workshop’s focus on time series analysis is particularly relevant in today’s data-driven landscape. As industries increasingly rely on temporal data for decision-making, the insights generated from this workshop can lead to significant advancements in methodologies and applications. Topics such as anomaly detection, forecasting, and causal analysis are crucial for enhancing the reliability and interpretability of AI systems in real-world scenarios.
Conclusion
The AAAI 2026 Workshop on AI for Time Series represents a unique opportunity for professionals and academics alike to engage with cutting-edge research and innovations in the field. Interested parties are encouraged to prepare their submissions and participate in shaping the future of AI applications in time series analysis.
Category: CFP & Deadlines
Tags: time-series, ai, machine-learning, aaai, forecasting, data-analysis, multimodal-ai, applications, algorithms, theory
