基于趋势符号聚合近似的卫星时序数据分类方法

As the main symbolic representation method widely used in time series data mining, the Symbolic Aggregation Approximation (SAX) uses the mean value of segments as the symbolic representation. Since it is impossible to distinguish different time series that have different trends but the same mean value, it may lead to incorrect classification. This paper presents an improved symbol representation-Trend Symbol Aggregation Approximation (TrSAX), which integrates SAX and least squares method to describe the mean and slope value of the time series, and constructs the BOTS classifier. In addition, this paper analyzes the angle sequence, rotation speed sequence, and current sequence in the satellite analog telemetry time series data, and selects three datasets similar to these three sequences from the UCR public dataset for classification experiment verification. They are compared with the 1-NN classification methods using SAX, two improved SAX, classic Euclidean Distance (ED) and Dynamic Time Warping (DTW). The results show that the classification error rate of the proposed BOTS classification method is significantly lower than the other five classification methods.