Composite metric method for time series similarity measurement based on symbolic aggregate approximation

Abstract

Key point-based Symbolic Aggregate approximation(SAX) improving algorithm(KP_SAX) uses key points to measure point distance of time series based on SAX,which can measure the similarity of time series more effectively.However,it is too short of information about the patterns of time series to measure the similarity of time series reasonably.To overcome the defects,a composite metric method of time series similarity measurement based on SAX was proposed.The method synthesized both point distance measurement and pattern distance measurement.First,key points were used to further subdivide the Piecewise Aggregate Approximation(PAA) segments into several sub-segments,and then a triple including the information about the two kinds of distance measurement was used to represent each sub-segment.Finally a composite metric formula was used to measure the similarity between two time series.The calculation results can reflect the difference between two time series more effectively.The experimental results show that the proposed method is only 0.96% lower than KP_SAX algorithm in time efficiency.However,it is superior to the KP_SAX algorithm and the traditional SAX algorithm in differentiating between two time series.