基于局部均值分解和改进小波熵的时序SAX模型

符号聚合近似表示法是提取时间序列特征的重要方式。然而,传统的符号聚合近似表示法存在平均化分段数、同等对待划分区间,以及无法准确反映非平稳序列的突变信息等多项缺陷。鉴于此,通过引入局部均值分解和改进小波熵的分段算法,建立了一种新的时序SAX模型。该模型的基本原理是采用局部均值分解技术对原始序列进行去噪处理,利用滑动窗口阈值法获取分段数,并使用SAX表示法进行符号表示,利用KNN分类器实现分类性能测试。基于这一改进模型,进行了实证检验,实验结果表明,该模型能够有效提取序列的信息特征,具有较高的拟合度,达到了降维的目的,更重要的是,提高了KNN分类算法在SAX表示法中分类的准确率。

A time series SAX model based on local mean decomposition and improved wavelet entropy

Symbolic aggregate approximation is an important way to extract time series features. However, traditional symbol aggregation approximation methods have many defects, such as the averaged segment number, equal segmentation interval, and mutation information that cannot accurately reflect the non-stationary sequence. Aiming at the abovementioned problems, we establish a new time series SAX model by introducing the local mean decomposition and a segmentation algorithm for improving wavelet entropy. The basic principle of the model is to denoise the original sequences by the local mean decomposition technique, obtain the segmentation number by the sliding window threshold method, and use SAX notation to represent the symbols, and the KNN classifier is used to realize classification performance test. Based on this improved model, we carry out an empirical test. The results show that the algorithm can effectively extract the information features of the sequences, has a high fitness degree, and achieves the purpose of dimensionality reduction. And more importantly, it improves the classification accuracy of the KNN classification algorithm in SAX notation.