Aiming at the complex time correlation of time series data and the problems of poor accuracy and instability of training in existing anomaly detection models,an unsupervised time series data anomaly detection model combining BiLSTM and WGAN-GP was proposed.BiLSTM was used as the basic network of generator and critic to capture the time correlation of time series data.To ensure the stability of the training process,the Wasserstein distance was used to replace the original measurement method,and the gradient penalty was added to the critic loss.The reconstruction loss and the discrimination loss were combined to define the anomaly function,and the local adaptive threshold method was used to distinguish the anomaly to improve the accu-racy of anomaly detection.To verify the performance of this model,experiments were carried out on five kinds of data sets invol-ving various domains.The results show that this model has the highest averaged F1 score compared with that of Arima,LSTM and other methods.
关键词
BiLSTM/WGAN-GP/时间相关性/稳定性/无监督/时序数据/异常检测
Key words
BiLSTM/WGAN-GP/temporal correlation/stability/unsupervised/time series data/anomaly detection
维普
万方数据