Time series pattern discovery by a PIP-based evolutionary approach

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/92490

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题名:	Time series pattern discovery by a PIP-based evolutionary approach
作者:	Chen, Chun-Hao;Tseng, Vincent S.;Yu, Hsieh-Hui;Hong, Tzung-Pei
贡献者:	淡江大學資訊工程學系
关键词:	Genetic algorithm;Segmentation;Time series;Clustering;Perceptually important points
日期:	2013-09
上传时间:	2013-10-16 15:16:00 (UTC+8)
出版者:	Heidelberg: Springer
摘要:	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.
關聯:	Soft Computing 17(9), pp.1699-1710
DOI:	10.1007/S00500-013-0985-y
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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