A novel SAX based time streams similarity approach

SAX (Symbolic Aggregate approXimation) is a kind of Symbolic time series similarity measurement method, which can not effectively distinguish the similarity between series in the circumstance of the corresponding value being similar between two sub-segment of time series. In this work, we proposed a novel time streams similarity approach based on SAX which was named KP_SAX. The similarity distance of KP_SAX described not only the statistical discipline of time series numerical change, but also the form changes of time series. The results show the superiority of our approaches as compared to the similarity measures of SAX and provide our promising results.