A PIP-based evolutionary approach for time series segmentation and pattern discovery

Hsieh Hui Yu*, S. Tseng, Chun Hao Chen, Tzung Pei Hong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


In the past, we proposed a time series segmentation approach by combining the clustering technique, the Discrete Wavelet Transformation (DWT) and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a PIP-based evolutionary approach, which uses Perceptually Important Points (PIP) instead of DWT, to effectively adjust the length of subsequences for finding appropriate segments and patterns and avoiding some problems in the previous approach. For achieving the purpose, the enhanced suitability factor in the fitness function which is modified from the previous approach, is designed. Experimental results on a real financial dataset also show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationICS 2010 - International Computer Symposium
Number of pages6
StatePublished - 1 Dec 2010
Event2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan
Duration: 16 Dec 201018 Dec 2010

Publication series

NameICS 2010 - International Computer Symposium


Conference2010 International Computer Symposium, ICS 2010


  • Clustering
  • Genetic algorithm
  • Perceptually important points
  • Segmentation
  • Time series

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