A fast method for change point detection from large-scale time series based on Haar Wavelet and Binary Search Tree (HWBST)

Generally, Change Point (CP) detection is time-consuming, especially from large-scale time series. In this paper, a fast method of CP detection is proposed based on Haar Wavelet (HW) and Binary Search Tree (BST), named HWBST. In this method, by multi-level HW, a Binary Search Tree, termed BSTcD, is constructed from a diagnosed time series, and two binary search criteria are introduced to detect abrupt change from root to leaf nodes in BSTcD. Then, the sensitivity and accuracy of HWBST are analyzed and evaluated on the simulated and Electrocardiogram (ECG) time series. The results show that HWBST has better performance than HW, KS, and T statistic methods, in terms of computation time, error, accuracy etc.