Multi-Frequency Decomposition with Fully Convolutional Neural Network for Time Series Classification

Abstract

Fully convolutional neural network (FCN) has achieved state-of-the-art performance in the task of time series classification without any heavy preprocessing. However, the FCN cannot effectively capture features of different frequencies. Therefore, this paper proposed a novel FCN structure based on the multi-frequency decomposition (MFD) method. In order to extract more features of different frequencies, the MFD based on real fast Fourier transform (RFFT) is set as a layer of the FCN to decompose the original signal into $\mathrm{n}$ sub-signals of different frequency bands. And then the improved FCN fuse those features of different frequencies together to obtain time series classification. Finally, compared with the existing state-of-the-art methods, the proposed method is effectively verified through some datasets in UCR Time Series Classification archive.