A pattern distance-based evolutionary approach to time series segmentation
- Authors
- Yu, Jingwen; Yin, Jian; Zhou, Duanning; Zhang, Jun
- Issue Date
- Aug-2006
- Publisher
- Springer Verlag
- Citation
- Intelligent Control and Automation International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August, 2006, pp 797 - 802
- Pages
- 6
- Indexed
- SCI
SCOPUS
- Journal Title
- Intelligent Control and Automation International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August, 2006
- Start Page
- 797
- End Page
- 802
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/117824
- DOI
- 10.1007/11816492_99
- Abstract
- Time series segmentation is a fundamental component in the process of analyzing and mining time series data. Given a set of pattern templates, evolutionary computation is an appropriate tool to segment time series flexibly and effectively. In this paper, we propose a new distance measure based on pattern distance for fitness evaluation. Time sequence is represented by a series of perceptually important points and converted into piecewise trend sequence. Pattern distance measures the trend similarity of two sequences. Moreovhttps://link.springer.com/chapter/10.1007/978-3-540-37256-1_99er, experiments are conducted to compare the performance of pattern-distance based method with the original one. Results show that pattern distance measure outperforms the original one in correct match, accurate segmentation. © Springer-Verlag Berlin/Heidelberg 2006.
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