Unsupervised detecting anomalies in multivariate time series by Robust Convolutional LSTM Encoder-Decoder (RCLED)

Monitoring modern industrial systems generates a large amount of multivariate time series data. One of the critical tasks of monitoring these systems is anomaly detection. The auto-encoder has emerged as a promising solution for detecting anomalies in systems lacking explicit anomaly data in their historical records; however, its performance can be sensitive to noisy data. This work aims at developing a robust anomaly detection model based on the autoencoder for multivariate time series. The autoencoder architecture consists of convolution, long short-term memory, and self-attention layers for better extraction of complex features from multivariate time series. In addition, a novel training technique, inherited from the idea of robust principal component analysis, was developed to efficiently train the proposed model when the input data is affected by noise. To evaluate the model’s performance, we tested it on two data sets: synthetic, real-industrial, and three public data. We also compare the performance of our model to that of the other state-of-the-art models. The results show that the proposed model outperforms all the latest models, especially when the noise level is considerable.