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Time series pattern discovery by a PIP-based evolutionary approach

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Abstract

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.

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Acknowledgments

This research was supported by the National Science Council of the Republic of China under contract NSC 96-2213-E-390-003.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Tamkang University, Taipei, 251, Taiwan, ROC

    Chun-Hao Chen

  2. Department of Computer Science and Information Engineering National, Cheng-Kung University, Tainan, 701, Taiwan, ROC

    Vincent S. Tseng & Hsieh-Hui Yu

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, ROC

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan, ROC

    Tzung-Pei Hong

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  1. Chun-Hao Chen
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  2. Vincent S. Tseng
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  3. Hsieh-Hui Yu
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  4. Tzung-Pei Hong
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Corresponding author

Correspondence to Tzung-Pei Hong.

Additional information

Communicated by G. Acampora.

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Chen, CH., Tseng, V.S., Yu, HH. et al. Time series pattern discovery by a PIP-based evolutionary approach. Soft Comput 17, 1699–1710 (2013). https://doi.org/10.1007/s00500-013-0985-y

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