Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (8): 10-16.doi: 10.16180/j.cnki.issn1007-7820.2020.08.002

Previous Articles     Next Articles

Design and Implementation of a Fast Online Algorithm for Mutation Point Detection

ZOU Junchen,QI Jinpeng,LI Na,LIU Jialun,ZHU Houjie   

  1. School of Information Science & Technology,Donghua University,Shanghai 201620,China
  • Received:2019-05-21 Online:2020-08-15 Published:2020-08-24
  • Supported by:
    National Natural Science Foundation of China(61305081);National Natural Science Foundation of China(61104154);Natural Science Foundation of Shanghai(16ZR1401300);Natural Science Foundation of Shanghai(16ZR1401200)


The traditional TSTKS algorithm is an offline mutation point detection algorithm, which has low accuracy when there are multiple mutation points in the time series data. To solve this problem, TSTKS algorithm and sliding window theory were combined to propose an online detection method for fast time series data mutation points. The method used sliding window to divide the data into several sub-segments, and took TSTKS algorithm to detect the mutation point for each sub-segment according to the order of window, so as to realize the rapid multi-mutation points detection of time series data. The results showed that compared with the common algorithms, the proposed algorithm took less time, had lower relative error rate and higher hit rate in multiple mutation points detection

Key words: TSTKS algorithm, mutation point detection, trigeminal search tree, sliding window theory, time series data, online detection

CLC Number: 

  • TP311