Time series pattern discovery by a PIP-based evolutionary approach
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.
time series segmentation clustering genetic algorithm perceptually important points