Flexible Shapelets Discovery for Time Series Classification

Borui Cai, Guangyan Huang*, Maia Angelova Turkedjieva, Yong Xiang, Chi Hung Chi

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


Time series classification is important due to its pervasive applications, especially for the emerging Smart City applications that are driven by intelligent sensors. Shapelets are sub-sequences of time series that have highly predictive abilities, and time series represented by shapelets can better reveal the patterns thus have better classification accuracy. Finding shapelets is challenging as its computational in-feasibility, most existing methods only finds shapelets with a same length or a few fixed length shapelets because the searching space of shapelets with arbitrary length is too large. In this paper, we improve the time series classification accuracy by discovering shapelets with arbitrary lengths. We borrow the idea of Apriori algorithm in association rule learning, that is, the superset shapelet candidates of a poor predictive shapelet candidate also have poor predictive abilities. Therefore, we propose a Flexible Shapelets Discovery (FSD) algorithm to discover shapelets with varying lengths. In FSD, shapelet candidates having the lower bound of length are discovered, and then we extend them into arbitrary lengths shapelets as long as their predictive abilities increases. Experiments conducted on 6 UCR time series datasets demonstrate that the arbitrary length shapelets discovered by FSD achieves better classification accuracy than those using fixed length shapelets.

Original languageEnglish
Title of host publicationData Science - 6th International Conference, ICDS 2019, Revised Selected Papers
EditorsJing He, Philip S. Yu, Yong Shi, Xingsen Li, Zhijun Xie, Guangyan Huang, Jie Cao, Fu Xiao
Number of pages10
Publication statusPublished - 2 Feb 2020
Event6th International Conference on Data Science, ICDS 2019 - Ningbo, China
Duration: 15 May 201920 May 2019

Publication series

NameCommunications in Computer and Information Science
Volume1179 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference6th International Conference on Data Science, ICDS 2019

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.


  • Classification
  • Shapelet discovery
  • Time series


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