Attention-based Representation Learning for Time Series with Principal and Residual Space Monitoring

The encoder-decoder network is one of the most common deep learning models for time series representation learning and anomaly detection. However, it is hard to reconstruct time series, which is complex, correlated, and lacking in common patterns. In this paper, we apply the attention mechanism to rescale convolution layers and learn representation in the principal and the residual space. To avoid the reconstruction process, we define the residual space by the omitted segments according to the attention score in the encoder. We introduce the temporal information inside the token level and use sparse penalty to improve representation learning. We apply the proposed model to anomaly classification and fault detection experiments on two datasets, i.e. multivariate bearing fault dataset and UCRArchive profile dataset. The result shows that the representation learned by the proposed model is more likely to cluster by category, especially in the residual space. Compared to the baselines and state-of-the-art models, the proposed model has higher accuracy and recall in the limited-labeled situation, which illustrates the stability of the learned representation and its superiority in the downstream tasks.