Time series data are ubiquitous. In domains as diverse as finance, retail, entertainment, transportation and health care, we observe a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. Recent advances in diverse sensing technologies, ranging from remote sensors to wearables and social sensing, are generating a rapid growth in the size and complexity of time series archives. Thus, although time series analysis has been studied extensively, its importance only continues to grow. What is more, modern time series data pose significant challenges to existing techniques (e.g., irregular sampling in hospital records and spatiotemporal structure in climate data). Finally, time series mining research is challenging and rewarding because it bridges a variety of disciplines and demands interdisciplinary solutions. Now is the time to discuss the next generation of temporal mining algorithms. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.
The MiLeTS workshop will discuss a broad variety of topics related to time series, including:

• Time series forecasting and prediction using classical approaches
• Time series forecasting and prediction using LLMs
• Time series pattern mining and detection, representation, searching and indexing, classification, clustering, prediction, forecasting, and rule mining.
• Time series with special structure: spatiotemporal (e.g., traffic speeds at different locations), relational (e.g., patients with similar diseases), hierarchical, etc.
• Time series with sparse or irregular sampling, missing values at and not at random, and special types of measurement noise or bias.
• Time series that are multivariate, high-dimensional, heterogeneous, etc., or that possess other atypical properties.
• Time series analysis using less traditional approaches, such as deep learning and subspace clustering.
• Privacy preserving time series mining and learning.
• Online, high-speed learning and mining from streaming time series.
• Uncertain time series mining.
• Applications to high impact or relatively new time series domains, such as health and medicine, road traffic, and air quality.
• New, open, or unsolved problems in time series analysis and mining.