基于KPCA和G-G聚类的多元时间序列模糊分段

针对传统的Gath-Geva(G-G)模糊分段方法需要人为设置参数,对高维时间序列分段效率低的问题,提出一种基于核主元分析(KPCA)和G-G聚类的多元时间序列模糊分段方法.首先,该算法利用KPCA方法对多元时间序列进行特征提取,去除冗余及无关变量的影响;然后,通过近邻传播算法(AP)得到分段数目的上界;最后,将时间信息考虑在内,基于所提出的MDBI有效值指标以及G-G模糊聚类在低维多元时间序列上实现多元时间序列的最佳模糊分段.实验结果表明,所提出算法可以快速有效地检测出时间序列的某种突然和渐近变化的趋势,在准确性和运行效率方面均得到了提升.

Fuzzy segmentation of multivariate time series with KPCA and G-G clustering

The traditional Gath-Geva (G-G) fuzzy segmentation algorithm needs to set parameters and has low segmentation efficiency for high-dimensional time series. To address such matters, a fuzzy segmentation method for multivariate time series based on kernel principal component analysis(KPCA) and G-G clustering is proposed. Firstly, the KPCA is used to extract key features of multivariate time series to remove the impacts of redundant and irrelevant variables. Then, the upper bound for the number of segments is determined using the affinity propagation(AP). Finally, taking the time information into account, the segmentation of high-dimension multivariate time series in the framework of low-dimension time series is realized with the modified Davies-Bouldin index(MDBI) and G-G fuzzy clustering. The experimental results show that the proposed algorithm can detect some sudden and gradual change trend of time series quickly and effectively, which improves the accuracy and operation efficiency.