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A New Method for Processing Symbolization Time Series

Symbolic aggregate approximation is data dispersed dimension reduction method. Time series are equal and unequal length after dimensional reduction through difference methods. For calculating similarity of unequal-length symbolization time series, a new method is proposed. First, key point method is used to process time series for getting important information. Then, the new method of this paper is done to let them equal in local. Finally, SAX is utilized to symbolic time series and then calculate similarity of them. The experimental results show that this method is simple and effective and it extends the field of calculating the similarity of unequal length time series.