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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92490

    Title: Time series pattern discovery by a PIP-based evolutionary approach
    Authors: Chen, Chun-Hao;Tseng, Vincent S.;Yu, Hsieh-Hui;Hong, Tzung-Pei
    Contributors: 淡江大學資訊工程學系
    Keywords: Genetic algorithm;Segmentation;Time series;Clustering;Perceptually important points
    Date: 2013-09
    Issue Date: 2013-10-16 15:16:00 (UTC+8)
    Publisher: Heidelberg: Springer
    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.
    Relation: Soft Computing 17(9), pp.1699-1710
    DOI: 10.1007/S00500-013-0985-y
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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