Anomaly Detection for Non-Stationary and Non-Periodic Univariate Time Series

Yu Lin Li, Jehn Ruey Jiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

10 引文 斯高帕斯(Scopus)

摘要

This study proposes an anomaly detection method called wavelet autoencoder anomaly detection (WAAD) for non-stationary and non-periodic univariate time series. The method first applies discrete wavelet transform to time series of a sliding time window to obtain wavelet transform coefficients. It then uses an autoencoder to encode and decode (reconstruct) these coefficients. WAAD calculates the reconstruction error for every time window. An anomaly is assumed to occur for specific conditions of the errors. By five NAB datasets, the performance of WAAD is evaluated and compared with other methods to show its superiority.

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主出版物標題2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面177-179
頁數3
ISBN(電子)9781728180601
DOIs
出版狀態已出版 - 23 10月 2020
事件2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan
持續時間: 23 10月 202025 10月 2020

出版系列

名字2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

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???event.eventtypes.event.conference???2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
國家/地區Taiwan
城市Yunlin
期間23/10/2025/10/20

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