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- Title
Time series pattern discovery by a PIP-based evolutionary approach.
- Authors
Chen, Chun-Hao; Tseng, Vincent; Yu, Hsieh-Hui; Hong, Tzung-Pei
- 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.
- Subjects
TIME series analysis; DISCRETE wavelet transforms; GENETIC algorithms; EUCLIDEAN distance; PARTICLE swarm optimization
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2013, Vol 17, Issue 9, p1699
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-013-0985-y