A PIP-based evolutionary approach for time series segmentation and pattern discovery

Abstract

In the past, we proposed a time series segmentation approach by combining the clustering technique, the Discrete Wavelet Transformation (DWT) and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a PIP-based evolutionary approach, which uses Perceptually Important Points (PIP) instead of DWT, to effectively adjust the length of subsequences for finding appropriate segments and patterns and avoiding some problems in the previous approach. For achieving the purpose, the enhanced suitability factor in the fitness function which is modified from the previous approach, is designed. Experimental results on a real financial dataset also show the effectiveness of the proposed approach.

Feature recognition of the futures time series based on perceptually important points

“…PIP (Perceptually Important Points) identification process was first proposed in the reference [2], and commonly used in the financial application to complete the technical pattern matching. Reference [3] put forward a method that combined PIP with the genetic algorithm which can find segmentation and pattern from a time series automatically and adjust the length of subsequences to find appropriate segments and patterns. Reference [4] proposed the IPIP(Improvement of Perceptually Important Points) time series dimensionality reduction method, and on this basis, put forward the multi-dimensional index structure based on the Skyline-Index [5].…”