A Symbolic Representation Method to Preserve the Characteristic Slope of Time Series

In recent years many studies have been proposed for knowledge discovery in time series. Most methods use some technique to transform raw data into another representation. Symbolic representations approaches have shown effectiveness in speedup processing and noise removal. The current most commonly used algorithm is the Symbolic Aggregate Approximation (SAX). However, SAX doesn’t preserve the slope information of the time series segments because it uses only the Piecewise Aggregate Approximation for dimensionality reduction. In this paper, we present a symbolic representation method to dimensionality reduction and discretization that preserves the behavior of slope characteristics of the time series segments. The proposed method was compared with the SAX algorithm using artificial and real datasets with 1-nearest-neighbor classification. Experimental results demonstrate the method effectiveness to reduce the error rates of time series classification and to keep the slope information in the symbolic representation.