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Accelerator control systems generates and stores many time-series data related to the performance of an accelerator and its support systems. Many of these time series data have detectable change trends and patterns. Being able to timely detect and recognize these data change trends and patterns, analyse and predict the future data changes can provide intelligent ways to improve the controls system with proactive feedback/forward actions. With the help of advanced data mining and machinedoi:10.18429/jacow-icalepcs2019-mopha043 fatcat:3hmsj26tqnhtpe477irn2bozay