MiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Series

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dc.contributor.authorPurushotham, Sanjay
dc.contributor.authorLi, Yaguang
dc.contributor.authorChe, Zhengping
dc.date.accessioned2021-08-24T18:17:47Z
dc.date.available2021-08-24T18:17:47Z
dc.date.issued2021-08-14
dc.descriptionKDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Miningen_US
dc.description.abstractTime 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.en_US
dc.description.sponsorshipSanjay Purushotham is partially supported by the US National Science Foundation under grant IIS–1948399.en_US
dc.description.urihttps://dl.acm.org/doi/abs/10.1145/3447548.3469485en_US
dc.format.extent2 pagesen_US
dc.genreconferene papers and proceedingsen_US
dc.identifierdoi:10.13016/m2zxkv-z0zi
dc.identifier.citationPurushotham, 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/10.1145/3447548.3469485en_US
dc.identifier.urihttps://doi.org/10.1145/3447548.3469485
dc.identifier.urihttp://hdl.handle.net/11603/22653
dc.language.isoen_USen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.en_US
dc.subjecttime-series analysisen_US
dc.subjecttemporal data miningen_US
dc.subjectCOVID-19 time seriesen_US
dc.titleMiLeTS'21: 7th KDD Workshop on Mining and Learning from Time Seriesen_US
dc.typeTexten_US

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