Purushotham, SanjaySong, DongjinWen, QingsongHuan, JunShen, CongZohren, StefanNevmyvaka, Yuriy2024-10-282024-10-282024-08-24Purushotham, Sanjay, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, and Yuriy Nevmyvaka. “The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs.” In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 6733–34. KDD ’24. New York, NY, USA: Association for Computing Machinery, 2024. https://doi.org/10.1145/3637528.3671489.https://doi.org/10.1145/3637528.3671489http://hdl.handle.net/11603/36820Time series data has become ubiquitous across various fields such as healthcare, finance, entertainment, and transportation, driven by advancements in sensing technologies that enable continuous monitoring and recording. This growth in data size and complexity presents new challenges for traditional analysis techniques, necessitating the development of advanced, interdisciplinary temporal mining algorithms. The goals of this workshop are to: (1) highlight significant challenges in learning and mining from time series data, such as irregular sampling, spatiotemporal structures, and uncertainty quantification; (2) discuss recent developments in algorithmic, theoretical, statistical, and systems-based approaches for addressing these challenges, including both classical methods and large language models (LLMs); and (3) synergize research efforts by exploring both new and open problems in time series analysis and mining. This workshop will focus on both the theoretical and practical aspects of time series data analysis, providing a platform for researchers and practitioners from academia, government, and industry to discuss potential research directions, critical technical issues, and present solutions for practical applications. Contributions from related fields such as AI, machine learning, data science, and statistics are also included.2 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.UMBC MText