June 5, 2024, 4:43 a.m. | Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, Jianxin Wang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02318v1 Announce Type: new
Abstract: With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. In this setting, time series anomaly detection is practically important. It endeavors to identify deviant samples from the normal sample distribution in time series. Existing approaches generally assume that all the time series is available at a central location. However, we are witnessing the decentralized collection of time series due …

abstract anomaly anomaly detection applications arxiv cs.db cs.dc cs.lg data detection domains framework generated identify mobile normal sample samples sensing series time series type world

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