Symbolic Representation Based on Temporal Order Information for Time Series Classification

In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal information in the symbols. The proposed method was evaluated on a decision tree classification using the Symbolic Aggregate Approximation and Equal Fixed-Values Discretization approaches applied to 45 time series datasets that includes artificial and real-world data. The experimental results demonstrate the method effectiveness to improve the classification accuracy and the decision tree size for most datasets while preserving the temporal order information into symbolic representations.