Computer Science ›› 2022, Vol. 49 ›› Issue (7): 40-49.doi: 10.11896/jsjkx.210700226

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features

GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China
    Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China
  • Received:2021-07-23 Revised:2022-02-28 Online:2022-07-15 Published:2022-07-12
  • About author:GAO Zhen-zhuo,born in 1997,master,is a member of China Computer Federation.His main research interests include data mining and machine learning.
    LIU Hai-yang,born in 1987,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include data mining and so on.
  • Supported by:
    National Natural Science Foundation of China(61771058)and Beijing Natural Science Foundation of China(4214067).

Abstract: In recent years,the research on the classification of time series has attracted more and more attention.Advanced time series classification methods are usually based on great feature representations.Shapelet refers to the discriminative subsequences in time series,which can effectively express the local shape characteristics of time series.However,the high computational cost greatly limits the practicability of the Shapelet-based time series classification methods.In addition,traditional Shapelet can only describe the overall shape characteristics of the subsequence under Euclidean distance metric,so it is easy to be disturbed by noise and is difficult to mine other types of discriminative information contained in the subsequence.To deal with the aforementioned problems,a new time series classification algorithm,named random Shapelet forest embedded with canonical time series features,is proposed in this paper.The proposed algorithm is based on the following three key strategies:1)randomly select Shapelet and limit the scope of Shapelet to improve efficiency;2)embed multiple canonical time series features in Shapelet to improve the adaptability of the algorithm to different classification problems and make up for the accuracy loss caused by the random selection of Shapelet;3)build a random forest classifier based on the new feature representations to ensure the generalization ability of the algorithm.Experimental results on 112 UCR time series datasets show that the proposed algorithm is more accurate than the STC algorithm which is based on Shapelet exact search and the Shapelet transform technique,as well as many other types of state-of-the-art time series classification algorithms.Moreover,extensive experimental comparisons verify the significant advantages of the proposed algorithm in terms of efficiency.

Key words: Canonical time series features, Classification, Random forest, Shapelet, Time series

CLC Number: 

  • TP181
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