The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs
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Purushotham, Sanjay, Dongjin Song, Qingsong Wen, et al. “The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs.” Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, August 3, 2025, 6292–93. https://doi.org/10.1145/3711896.3737867.
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Time series data is now pervasive across domains such as healthcare, finance, entertainment, and transportation, driven by advances in sensing technologies that enable continuous data collection. The resulting increase in data volume and complexity poses significant challenges to traditional analysis methods, calling for the development of advanced, interdisciplinary approaches to temporal data mining. This workshop aims to: (1) identify key challenges in learning from time series data, including irregular sampling, spatiotemporal dependencies, and uncertainty quantification; (2) explore recent advances in algorithmic, statistical, theoretical, and systems-based solutions-ranging from classical methods to emerging techniques involving large language models (LLMs); and (3) foster collaboration by highlighting open problems and novel research directions in time series analysis. Bridging theory and practice, the workshop provides a platform for researchers and practitioners from academia, industry, and government to exchange ideas, discuss technical challenges, and showcase practical applications. Contributions from related areas such as AI, machine learning, data science, and statistics are strongly encouraged.