An iterative end point fitting based trend segmentation representation of time series and its distance measure

Haiyan Chen, Jinghan Du, Weining Zhang, Bohan Li

Abstract: Symbolic approximation representation is a key problem in time series which can significantly affect the accuracy and efficiency of data mining. However, since currently used methods divide the original sequence into segments with equal size, they ignore one of the most important features of time series: the trend. To overcome the defect of equal-sized segmenting, we present a trend segmentation representation based on Iterative End Point Fitting algorithm (IEPF-TSR). Particularly, we use iterative end point fitting (IEPF) algorithm to search the break point of each segment and get the trend segmentation. Then a triplet based symbolic representation is proposed for each segment which includes the start point, mean and trend. Moreover, we define a new distance measure method based on trend segmentation representation (TSR-DIST) which can suit for two representations with different lengths, and prove it to be the lower bound of Euclidean distance. The experimental results on UCR datasets show that the proposed representation and distance measure achieve better performance than the state-of-the-art methods in the classification accuracy and the dimensionality reduction ratio.