Sailfish: A Fast Bayesian Change Point Detection Framework with Gaussian Process for Time Series

By detecting the changing trend, Change point detection (CPD) can describe the underlying behavior of the system with time series (e.g., equipment failure detection, auxiliary medical diagnosis, and climate change detection, etc.). However, in the current big data environment, how quickly obtaining time-series data dependencies and accurately detecting change points is still a challenge. We propose Sailfish, an unsupervised change point detection framework based on the Gaussian process for time series data. In comparison with existing CPD designs, Sailfish has two novel features: 1) using the deep Gaussian process as a hidden variable transformer and 2) integrating the Gaussian process into a bi-LSTM cell for capturing past and future embedded feature trends. Our extensive experiments show that the Sailfish significantly outperforms five state-of-the-art CPD methods with a faster speed and higher accuracy on three public real-time-series datasets. Especially on the large volume of datasets, Sailfish can achieve up to a 60% reduction in training time and 13% F1-score improvement with little overhead compared to the state-of-the-arts.