关键词: shapelet, 趋势特征, 符号化, 多样化top-k查询, 时间序列分类

Abstract: Shapelet is a kind of recognizable time series sub-sequence, by identifying the local characteristics to achieve the purpose of accurate classification of time series. The original shapelet discovery algorithm has low efficiency, and much work has focused on improving the efficiency of shapelet discovery. However, for the time series with trend change, the typical time series representation is used for shapelet discovery, which tends to cause the loss of trend information in the sequence. In order to solve this problem, a new trend-based diversified top-k shapelet classification method was proposed. Firstly, the method of trend feature symbolization was used to represent the trend information of time series. Then, the shapelet candidate set was obtained according to the trend signature of the sequence. Finally, the most representative k shapelets were selected from the candidate set by introducing the diversifying top-k query algorithm. Experimental results of time series classification show that compared with the traditional classification algorithms, the accuracy of the proposed method was improved on 11 experimental data sets; compared with FastShapelet algorithm, the efficiency was improved, the running time of the proposed method was shortened, specially for the data with obvious trend information. The experimental results indicate that the proposed method can effectively improve the accuracy and the effciency of time series classification.

Key words: shapelet, trend feature, symbolization, diversified top-k query, time series classification