Multi-view Deep Markov Models for Time Series Anomaly Detection

We consider the problem of multi-view anomaly detection for multi-view time series data. This task aims to find time steps in time series instances that have inconsistent features across multiple views. To solve the problem, we propose a multi-view deep Markov model that can learn sequential structures in complex high-dimensional multi-view time series data. In our proposed model, each view is modeled by a sequence of latent states and the state transition function is shared across views. Therefore, the inconsistencies in views data will lead to inconsistencies in transitions between latent states across views, which makes the likelihood of abnormal time steps not high. We rely on that property to compute the multi-view anomaly score of each time step in all time series instances. The extensive experiments show that the proposed model is effective in detecting multi-view anomalies in time series data.