Time series data are becoming ubiquitous in numerous realworld 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 cover on both the oretical and practical aspects of time series data analysis and aim to trigger research innovations in theories, algorithms, and applications. This year, we will have a particular focus on foundation models as well as large language models (LLMs), and would like to discuss their potential impact and how they can be applied to varieties of time series 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
• Foundation models for time series
• Large language models (LLMs) for 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.