An anomaly detection model of time series based on dual attention and deep autoencoder
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 a-nomalies.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 se-ries.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 bench-mark model Tad-GAN by 25.25%in terms of detection performance.
anomaly detectiondual-layer attention mechanismautoencoderconvolutional neural networksbidirectional-gated recurrent unit
万方数据
维普
