Zobrazeno 1 - 4
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pro vyhledávání: '"Zhou Shengchen"'
Time-series anomaly detection plays a vital role in monitoring complex operation conditions. However, the detection accuracy of existing approaches is heavily influenced by pattern distribution, existence of multiple normal patterns, dynamical featur
Externí odkaz:
http://arxiv.org/abs/2202.02721
Publikováno v:
In Information Sciences March 2019 477:220-233
Autor:
Chen Kang, Peng Huaiwu, Zhang Junfeng, Xu Xinxin, Zhou Shengchen, Ruan Jingxin, Li Biao, Wang Yueshe
Publikováno v:
Energy Science & Engineering. 10:3918-3927
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd4014f102687a822e0c19ab714a3ad3
https://doi.org/10.1002/ese3.1255
https://doi.org/10.1002/ese3.1255
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783319987750
The problem of detecting anomalies in time series has attracted much attention recently due to its numerous applications. In this paper, we proposed a novel framework based on nonlinear methods. Firstly, we use time-dependent URP (Unthreholded Recurr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9784f6e3c4b664a3c62732de8d0085ce
https://doi.org/10.1007/978-3-319-98776-7_52
https://doi.org/10.1007/978-3-319-98776-7_52