Time series pattern discovery by a PIP-based evolutionary approach

Chun Hao Chen, Vincent Shin-Mu Tseng, Hsieh Hui Yu, Tzung Pei Hong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Time series are an important and interesting research field due to their many different applications. In our previous work, we proposed a time-series segmentation approach by combining a clustering technique, discrete wavelet transformation (DWT) and a genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a perceptually important points (PIP)-based evolutionary approach, which uses PIP instead of DWT, to effectively adjust the length of subsequences and find appropriate segments and patterns, as well as avoid some problems that arose in the previous approach. To achieve this, an enhanced suitability factor in the fitness function is designed, modified from the previous approach. The experimental results on a real financial dataset show the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)1699-1710
Number of pages12
JournalSoft Computing
Issue number9
StatePublished - 1 Sep 2013


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

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