Abstract: Currently, time series data often exhibit weak periodicity and highly complex correlation features, making it challenging for traditional time series anomaly detection methods to detect such anomalies. To address this issue, a novel unsupervised time series anomaly detection model (DA-CBG-AE) is proposed. Firstly, a novel sliding window approach is used to set the window size for time series periodicity. Secondly, convolutional neural networks are employed to extract high-dimensional spatial features from the time series. Then, a bidirectional gated recurrent unit network with stacked Dropout is proposed as the basic architecture of the autoencoder to capture the correlation features of the time series. Finally, a dual-layer attention mechanism is introduced to further extract features and select more critical time series, thereby improving the accuracy of anomaly detection. To validate the effectiveness of the model, DA-CBG-AE is compared with six benchmark models on eight datasets. The experimental results show that DA-CBG-AE achieves the optimal F1 value (0.863) and outperforms the latest benchmark model Tad-GAN by 25.25% in terms of detection performance.

Key words: anomaly detection, dual-layer attention mechanism, autoencoder, convolutional neural networks, bidirectional-gated recurrent unit ,