Time Series Anomaly Detection Via Hypothesis Testing for Dynamical Systems

Real world systems---such as robots, weather, energy systems and stock markets---are complicated and high-dimensional. Hence, without prior knowledge of the system dynamics, detecting or forecasting abnormal events from the sequential observations of the system is challenging. In this work, we address the problem caused by high-dimensionality via viewing time series anomaly detection as hypothesis testing on dynamical systems. This perspective can avoid the dimension of the problem from increasing linearly with time horizon, and naturally leads to a novel anomaly detection model, termed as DyAD (Dynamical system Anomaly Detection). Furthermore, as existing time-series anomaly detection algorithms are usually evaluated on relatively small datasets, we released a large-scale one on detecting battery failures in electric vehicles. We benchmarked several popular algorithms on both public datasets and our released new dataset. Our experiments demonstrated that our proposed model achieves state-of-the-art results.