Semi-supervised time series anomaly detection based on statistics and deep learning

Jehn Ruey Jiang, Jian Bin Kao, Yu Lin Li

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Thanks to the advance of novel technologies, such as sensors and Internet of Things (IoT) technologies, big amounts of data are continuously gathered over time, resulting in a variety of time series. A semi-supervised anomaly detection framework, called Tri-CAD, for univariate time series is proposed in this paper. Based on the Pearson product-moment correlation coefficient and Dickey–Fuller test, time series are first categorized into three classes: (i) periodic, (ii) stationary, and (iii) non-periodic and non-stationary time series. Afterwards, different mechanisms using statistics, wavelet transform, and deep learning autoencoder concepts are applied to different classes of time series for detecting anomalies. The performance of the proposed Tri-CAD framework is evaluated by experiments using three Numenta anomaly benchmark (NAB) datasets. The performance of Tri-CAD is compared with those of related methods, such as STL, SARIMA, LSTM, LSTM with STL, and ADSaS. The comparison results show that Tri-CAD outperforms the others in terms of the precision, recall, and F1-score.

Original languageEnglish
Article number6698
JournalApplied Sciences (Switzerland)
Volume11
Issue number15
DOIs
StatePublished - 1 Aug 2021

Keywords

  • Anomaly detection
  • Autoencoder
  • Deep learning
  • Internet of Things
  • Sensors
  • Time series
  • Wavelet transform

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