AutoSW: a new automated sliding window-based change point detection method for sensor data
Ebrahim Behrouzian Nejad, CARLA VANESSA DA SILVA, Arlete S. Rodrigues, Alípio Jorge, Inês Dutra · 2022
Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested.