A similarity measure for time series based on symbolic aggregate approximation and trend feature

Abstract

Symbolic Aggregate Approximation (SAX) is a popular method in many time series data mining applications, but its representation of time series is incomplete because it doesn’t take the trend feature of time series segment into consideration. In this paper, an improved SAX for time series based on trend feature (TRSAX) is proposed to solve this problem. Specifically, we firstly extract trend features of equal-sized time series segments by two sine functions to quantitatively measure different trends. Then, we define our TRSAX distance by integrating a weighted trend distance into the original SAX distance. To show the effectiveness and efficiency, TRSAX is compared with other traditional methods Euclidean distance (ED), dynamic time warping (DTW), SAX and extended SAX (ESAX) on diverse time series data sets. The experimental results show that compared with ED, DTW, SAX, and ESAX, our method TRSAX has a better performance.