The 9th SIGKDD International Workshop on Mining and Learning from Time Series
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2023-08-04
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Citation of Original Publication
Purushotham, Sanjay, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, and Yuriy Nevmyvaka. “The 9th SIGKDD International Workshop on Mining and Learning from Time Series.” In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5876–77. KDD ’23. New York, NY, USA: Association for Computing Machinery, 2023. https://doi.org/10.1145/3580305.3599214.
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Abstract
Time series data has become pervasive across domains such as finance, transportation, retail, entertainment, and healthcare. This shift towards continuous monitoring and recording, fueled by advancements in sensing technologies, necessitates the development of new tools and solutions. Despite extensive study, the importance of time series analysis continues to increase. However, modern time series data present challenges to existing techniques, including irregular sampling and spatiotemporal structures. Time series mining research is both challenging and rewarding as it connects diverse disciplines and requires interdisciplinary solutions. The goals of this workshop are to (1) highlight the significant challenges that underpin learning and mining from time series data (e.g., irregular sampling, spatiotemporal structure, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) to synergize the research activities and discuss both new and open problems in time series analysis and mining. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from academia and industry to discuss potential research directions and critical technical issues and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.