A New Segmentation Algorithm to Stock Time Series Based on PIP Approach

Stock time series segmentation is one of the fundamental components in stock time series data mining. And the segmentation is often used for the trend analysis. In this paper, a new stock time series segmentation algorithm is proposed based on PIP(Perceptually Important Point) approach. This proposed segmentation method contributes to containing both the important data points and the primitive trends like uptrend and downtrend, while most of the current algorithms only contain one aspect of that. The proposed segmentation algorithm is proved to be more efficient and effective in reserving the trends and less complexity than those combined split-and- merge piecewise linear approximation segmentation algorithms.