Nearest Neighbor Optimal Smooth Denoising Dynamic Classification Method for Financial Time Series

Abstract

In view of the problem of excessive noise in financial time series, this paper proposes a nearest neighbor dynamic time warping classification method for financial time series based on the optimal smooth denoising model (osdDTW2). First, the optimal smooth denoising model is improved, and then the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the time series signal. Then, the improved optimal smooth denoising model is used to construct a low-pass filter to do the denoising of the time series. After being denoised, the time series are aligned by dynamic time warping (DTW), Finally, the nearest neighbor method is used for classification. This paper also uses the UCR datasets to verify the effectiveness of the proposed method and applies it method to financial time series classification. The experimental results suggest that osdDTW2 ([Formula: see text]) can improve the effectivness of the benchmark algorithm (DTW) to some extent.