Financial time series classification method based on low‐frequency approximate representation

Abstract

AbstractAiming at the mode mixing problems of high frequency information caused by fluctuation agglomeration and pointed peak thick tail of financial time series, a time series classification method based on low frequency approximate representation is proposed. The steps are as follows. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the time series into a series of modal components and residual terms, and the permutation entropy of the modal components and residual terms was calculated. And the permutation entropy was clustered into two categories by Fisher's optimal segmentation method. Then, according to the clustering results of permutation entropy, the corresponding modal components and residual terms were integrated into high frequency information and low frequency information, so as to realize the adaptive extraction of low frequency information. Secondly, distance matrices were obtained by Euclidean distance (ED) or dynamic time warping (DTW) for low frequency information, and then the nearest neighbor 1‐NN algorithm was used to classify time series.