Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

Jian Bin Kao, Jehn Ruey Jiang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

19 引文 斯高帕斯(Scopus)

摘要

This paper proposes an anomaly detection framework for univariate time series data. Based on the Dickey-Fuller test, fast Fourier transform (FFT), and Pearson product-moment correlation coefficient, data are classified into three classes, namely (1) stationary, (2) periodic and (3) non-stationary and non-periodic time series. Different schemes using statistics and gated recurrent unit (GRU) deep learning concepts are applied to different class of time series for performing anomaly detection. The proposed framework outperforms related methods, namely STL, SARIMA, LSTM, LSTM with STL, and ADSaS, in almost all measurements for five Numenta Anomaly Benchmark(NAB) datasets.

原文???core.languages.en_GB???
主出版物標題2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面404-407
頁數4
ISBN(電子)9781728125015
DOIs
出版狀態已出版 - 10月 2019
事件2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan
持續時間: 3 10月 20196 10月 2019

出版系列

名字2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

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???event.eventtypes.event.conference???2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
國家/地區Taiwan
城市Yunlin
期間3/10/196/10/19

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