Abstract: Time series dimentionality reduction technology is used to resolve high dimensionality time series．Symbolic aggregate appro ximation (SAX representation) is a time series dimensionality reduction technique which benefits from its brief representation in dimensionality reduction and highperformance lower bound distance algorithm,but there is a question that the number of segments,a parameter in SAX,is set artificially based on the characteristic of individual time series．To solve this problem,similarity measure algorithm of time series based on binarydividing SAX was presented by introducing sliding window and statistical methods．The experimental results show that binarydividing SAX algorithm not only solves the difficulty to choose the number of segments,but also reduces the complexity of time series representation in dimensionality reduction and improves classification accuracy by using the SAX algorithm in a variety of time series data.

Key words: Dimensionality reduction，Symbolic aggregate approximation，Sliding window