Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

Jian Bin Kao; Jehn Ruey Jiang

doi:10.1109/ECICE47484.2019.8942727

Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

Jian Bin Kao, Jehn Ruey Jiang

資訊工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

22 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	https://doi.org/10.1109/ECICE47484.2019.8942727
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 2019 → 6 10月 2019

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

???event.eventtypes.event.conference???

???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	3/10/19 → 6/10/19

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10.1109/ECICE47484.2019.8942727

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Kao, J. B., & Jiang, J. R. (2019). Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. 於 T.-H. Meen (編輯), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 (頁 404-407). 文章 8942727 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECICE47484.2019.8942727

Kao, Jian Bin ; Jiang, Jehn Ruey. / Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. 編輯 / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. 頁 404-407 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

@inproceedings{6dbac46a7cf14a47995163af87209fba,

title = "Anomaly Detection for Univariate Time Series with Statistics and Deep Learning",

abstract = "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.",

keywords = "anomaly detection, deep learning, gated recurrent unit neural network, time series",

author = "Kao, {Jian Bin} and Jiang, {Jehn Ruey}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 ; Conference date: 03-10-2019 Through 06-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ECICE47484.2019.8942727",

language = "???core.languages.en_GB???",

series = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "404--407",

editor = "Teen-Hang Meen",

booktitle = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",

}

Kao, JB & Jiang, JR 2019, Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. 於 T-H Meen (編輯), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019., 8942727, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Institute of Electrical and Electronics Engineers Inc., 頁 404-407, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Yunlin, Taiwan, 3/10/19. https://doi.org/10.1109/ECICE47484.2019.8942727

Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. / Kao, Jian Bin; Jiang, Jehn Ruey.
2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. 編輯 / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. p. 404-407 8942727 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

TY - GEN

T1 - Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

AU - Kao, Jian Bin

AU - Jiang, Jehn Ruey

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

KW - anomaly detection

KW - deep learning

KW - gated recurrent unit neural network

KW - time series

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M3 - 會議論文篇章

AN - SCOPUS:85078575736

T3 - 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

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BT - 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

Y2 - 3 October 2019 through 6 October 2019

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Kao JB, Jiang JR. Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. 於 Meen TH, 編輯, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 404-407. 8942727. (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). doi: 10.1109/ECICE47484.2019.8942727

Anomaly Detection for Univariate Time Series with Statistics and Deep Learning

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