Purushotham, SanjayLi, YaguangChe, Zhengping2021-08-242021-08-242021-08-14Purushotham, Sanjay; Li, Yaguang; Che, Zhengping; MiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Series; KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 14 August 2021, Pages 4151–4152; https://doi.org/10.1145/3447548.3469485https://doi.org/10.1145/3447548.3469485http://hdl.handle.net/11603/22653KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningTime series data are ubiquitous. Rapid 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. This has resulted in a fundamental shift away from parsimonious, infrequent measurement to nearly continuous monitoring and recording. This demands development of new tools and 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, and uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) synergize the research activities and discuss both new and open problems in time series analysis and mining.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.time-series analysistemporal data miningCOVID-19 time seriesText