The Fourier Decomposition Method for nonlinear and nonstationary time series analysis

TL;DR

This paper introduces a Fourier Decomposition Method (FDM) that effectively analyzes nonlinear and nonstationary time series by decomposing data into intrinsic band functions, outperforming traditional methods like EMD.

Contribution

The paper proposes a novel Fourier Decomposition Method and a multivariate extension that preserve physical properties and provide detailed time-frequency-energy analysis.

Findings

FDM effectively analyzes nonlinear, nonstationary data.

MFDM preserves physical properties like scale and trend.

Results outperform EMD in simulations and real data analysis.

Abstract

Since many decades, there is a general perception in literature that the Fourier methods are not suitable for the analysis of nonlinear and nonstationary data. In this paper, we propose a Fourier Decomposition Method (FDM) and demonstrate its efficacy for the analysis of nonlinear (i.e. data generated by nonlinear systems) and nonstationary time series. The proposed FDM decomposes any data into a small number of `Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank based multivariate FDM (MFDM) algorithm, for the analysis of multivariate nonlinear and nonstationary time series, from the FDM. We also present an algorithm to obtain cutoff frequencies for MFDM. The MFDM algorithm is generating finite number of band limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods produce the results in a time-frequency-energy distribution that reveal the intrinsic structures of a data. Simulations have been carried out and comparison is made with the Empirical Mode Decomposition (EMD) methods in the analysis of various simulated as well as real life time series, and results show that the proposed methods are powerful tools for analyzing and obtaining the time-frequency-energy representation of any data.