New method of nonlinear time series clustering
一种新的非线性时间序列的聚类方法

Most of the popular clustering methods are designed for the linear time series, assuming that the stationary time series can be fitted by linear model. In fact, the true word is nonlinear. This paper proposed a cluster algorithm which could be used on nonlinear time series clustering. This new proposal was KS2D distance measure based on KS 2-dimensional statistics, which took the temporal correlation structure of nonlinear time series into account. It was a nonparametric and robust measure to recognize the similarity of shape and dynamic dependence behavior of time series. Analogous to the theoretical analysis, the experiments on simulated data perform well.