Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220700094-7.doi: 10.11896/jsjkx.220700094

• Big Data & Data Science • Previous Articles     Next Articles

Anomaly Detection of Time-series Based on Multi-modal Feature Fusion

ZHANG Guohua, YAN Xuefeng, GUAN Donghai   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Software New Technology and Industrialization Collaborative Innovation Center,Nanjing 211106,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:ZHANG Guohua,born in 1995,postgraduate.His main research interests include machine learning and anomaly detection. YAN Xuefeng,born in 1975,Ph.D,professor,is a member of China Computer Federation.His main research interests include intelligent computing,model-based systems engineering,simulation,and evaluation.

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Abstract: Effective anomaly detection of multivariate time series is important for data mining analysis.However,most of the exi-sting detection methods are based on single modality,they cannot effectively utilize the distribution information of time series in multi-modal space.For multi-modal features,there is no effective adaptive fusion method and extraction method of spatial-temporal dependence.In this paper,a time series anomaly detection method based on multi-modal feature fusion is proposed.The multi-modal feature adaptive fusion module is established,it can adaptively fuse the multi-modal features through convolution network and soft selection mode.The spatial-temporal attention module is proposed,it is composed of temporal attention and spatial attention.It extracts spatial-temporal dependence of the multi-modal features and outputs the spatial-temporal attention vector.Then the model prediction results are obtained based on the spatial-temporal attention vector.By learning the distribution of normal samples,anomaly detection result is obtained according to the error measure between the predicted values and the real values.The proposed method is compared with other state-of-the-art models on four public datasets,and results demonstrate its effectiveness.

Key words: Time series, Anomaly detection, LSTM, 1D-CNN, Attention mechanism

CLC Number: 

  • TP391
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