AutoSW: a new automated sliding window-based change point detection method for sensor data
2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)(2022)
摘要
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.
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关键词
change point detection,time series,sensor data,machine learning,sensor industry
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