The representation task in time series data mining has been a critical issue because the direct manipulation of continuous, high-dimensional data is extremely difficult to complete efficiently. One time series representation approach is a symbolic representation called the Symbolic Aggregate approXimation (SAX). The main function of SAX is to find the appropriate numbers of alphabet symbols and word size that represent the time series. The aim is to achieve the largest alphabet size and maximum word length with the minimum error rate.