A cluster-based genetic approach for segmentation of time series and pattern discovery

In the past, we proposed a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose an enhanced approach to solve the problems that may occur during the evolution process. Two factors, namely the density factor and the distortion factor, are used to solve them. The distortion factor is used to avoid the distortion of the segments and the density factor is used to avoid generation of meaningless patterns. The fitness value of a chromosome is then evaluated by the distances of segments and these two factors. Experimental results on a financial dataset also show the effectiveness of the proposed approach.