Spatial-temporal Attention Model Based on Transformer Architecture for Anomaly Detection in Multivariate Time Series Data,ERICDATA高等教育知識庫
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篇名
Spatial-temporal Attention Model Based on Transformer Architecture for Anomaly Detection in Multivariate Time Series Data
並列篇名
Spatial-temporal Attention Model Based on Transformer Architecture for Anomaly Detection in Multivariate Time Series Data
作者 Lai ZengXiaomei Yang
英文摘要

Anomaly detection with multivariate time series data collected by multi-sensors is a challenging problem due to the complexity and high dimension of data and the difficulty of manually labelling data. This paper proposes a novel unsupervised anomaly detection model using spatial-temporal self-attention based on transformer architecture, denoted the biself-attention anomaly detection (BSAAD) model. The BSAAD model not only utilizes a time-step encoder with a self-attention mechanism to capture temporal correlation but also constructs a sensor encoder with a self-attention mechanism to capture spatial correlation among multivariate time series data. To amplify the reconstruction errors of anomalous points during network training, a two-phase training style with an adversarial training strategy is used to improve the anomaly detection performance of the BSAAD model. Experiments on six multivariate time series datasets show that the BSAAD model outperforms state-of-the-art anomaly detection methods.

 

起訖頁 193-207
關鍵詞 biself-attentiontransformeranomaly detectionmultivariate time series dataBSAAD model
刊名 電腦學刊  
期數 202406 (35:3期)
DOI 10.53106/199115992024063503014   複製DOI
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