基于多级Haar小波变换与KS统计的突变点快速探测方法

Fast Abrupt-point Detection Method Based on Multistage Haar Wavelet Transform and KS Statistic

结合多级Haar小波变换与KS统计理论,提出一种对时序数据突变点的快速探测方法(HWKS),对标准参考序列以及待检测序列分别构建均值二叉搜索树和差值二叉搜索树。基于改进的KS检验方法给出二叉树搜索的2种策略,进而构建实现时序数据突变点快速检测的HWKS理论框架。运用HWKS对模拟的时序数据进行检测,与HW方法、T方法和KS方法的比较结果表明,该方法在对时序数据的突变点进行检测时的误差较小、用时最短、准确度较高。

Combined with multi-level Haar wavelet transform and KS statistic theory,this paper proposes a fast detection method for time series data abrupt-point,that names as HWKS. The mean binary search tree and the difference binary search tree are constructed respectively for the standard reference sequence and the sequence to be detected. Based on the improved KS test method,two methods of binary tree search are proposed, and the HWKS theory framework for rapid detection of time series data mutation point is realized. HWKS is used to detect the simulated time series data.Comparison results on HW method,T method and KS method show that HWKS has less error, shorter time and higher accuracy when detecting time series data abrupt-point.