%0 Journal Article %A HUO Weigang %A WANG Huifang %T Time series anomaly detection method based on autoencoder and HMM %D 2020 %R 10.11772/j.issn.1001-9081.2019091631 %J Journal of Computer Applications %P 1329-1334 %V 40 %N 5 %X

To solve the issue that the existing symbolic methods of anomaly detection based on Hidden Markov Model (HMM) cannot well represent the original time series, an Autoencoder and HMM-based Anomaly Detection (AHMM-AD) method was proposed. Firstly, the time series samples were segmented by sliding window, and several time series segmented sample sets were formed according to the positions of the segmentations, and the autoencoder of each segmentation was trained by the segmented sample set of different positions on the normal time series. Then, the low-dimensional feature representation of each segmented time series sample was obtained by using the autoencoder, and through K-means clustering of low-dimensional feature representation vector sets, the symbolization of time series sample sets was realized. Finally, the HMM was generated based on the symbol sequence set of the normal time series, and the abnormal detection was carried out according to the output probability values of the test samples on the established HMM. The experimental results on multiple common benchmark datasets show that AHMM-AD improves the accuracy, recall rate, and F1 value by 0.172, 0.477 and 0.313 respectively compared to those of the HMM-based time series anomaly detection model, and has 0.108, 0.450 and 0.319 increasement in these three aspects respectively compared with the autoencoder-based time series anomaly detection model. The experimental results illustrate that AHMM-AD method can extract the nonlinear features in time series, solve the problem that the time series cannot be well represented during the symbolization process of existing HMM-based time series modeling, and also improve the performance of time series anomaly detection.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019091631