[CFP] Call for Papers — AAAI 2026 Workshop on AI for Time Series (AI4TS): Theory, Algorithms, and Applications

[Dongjin Song]

🚀 Call for Papers — AAAI 2026 Workshop on AI for Time Series (AI4TS): Theory, Algorithms, and Applications

📅 January 26, 2026 | Singapore | AAAI 2026
🌐 https://ai4ts.github.io/aaai2026

Workshop Paper Submission Due Date: October 22nd, 2025 (23:59pm AoE)
Notification of Paper Acceptance: November 5th, 2025 (23:59pm AoE)
Submission link:

https://cmt3.research.microsoft.com/AIforTS2026

We’re excited to announce the AAAI 2026 Workshop on AI for Time Series (AI4TS) — a premier venue at the intersection of Artificial Intelligence and Time Series Analysis co-organized by Min Wu, Qingsong Wen, Sanjay Purushotham, haifeng Chen, Cong Shen, Shirui Pan，Stefan Zohren, Yuriy Nevmyvaka, Niloy Biswas, and Li Xiaoli.

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.

Now in its 5th year, AI4TS will bring together leading researchers and practitioners to explore how foundation models, large language models, multimodal reasoning, and robust forecasting are reshaping the landscape of temporal learning and decision-making.

💡 Topics include:
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
Foundation and LLM-based time series models
Multimodal, spatio-temporal, and graph-temporal learning
Causal, interpretable, and trustworthy forecasting
Applications in science, health, climate, and finance

🧠 Submission Details are available on our website:
👉 https://ai4ts.github.io/aaai2026
Keynote Speakers, Panelists, and detailed programs will be available soon! We invite researchers, students, and industry experts to join us in Singapore to shape the future of AI for Time Series!