Time series anomaly detection with sequence reconstruction based state-space model

Recent advances in digitization has led to availability of multivariate time series data in various domains, in order to monitor operations in real time. Identifying abnormal data pattern and detect potential failures in these scenarios are important yet rather difficult tasks. We propose a novel unsupervised anomaly detection method for time series data. Our approach uses sequence encoder and decoder to represent the mapping between time series and hidden state, and learns bidirectional dynamics simultaneously by leveraging backward and forward temporal information in the training process. We further regularize the state space to place constraints on states of normal samples, and use Mahalanobis distance to evaluate abnormality level. Results on synthetic and real-world datasets show the superiority of the proposed method.