基于主成分分析的时间序列Shapelet提取方法

Shapelet序列分析为时间序列分类提供了一种快速分类的方法，但Shapelet序列抽取速度很慢，限制了它的应用范围。为了加快 Shapelet 序列的提取，提出了一种基于主成分分析的改进方法。首先运用主成分分析法（PCA）对时间序列数据集进行降维，采用降维后的数据表示原数据，然后对降维后的数据提取出最能代表类特征的Shapelet序列。实验结果表明：本方法在保证分类准确率的前提下，提高了运算速度。

Time Series Shapelet Extraction Based on Principal Component Analysis

Shapelet provides a fast classification method in time series classification, but the extraction of time series Shapelet is so slow that it restricts the application of the Shapelet. In order to speed up the extraction of time series Shapelet, an improved method is proposed based on the principal component analysis. Firstly, it uses the principal component analysis (PCA) to reduce the dimension of time series data set and chooses the reduced data to represent the original data. Secondly, it can extract the most discriminatory Shapelet sequence from the reduced data. Lastly, the experimental results show that the improved method improves the speed of the extraction and ensures the accuracy of classification.