Extreme-Point Symmetric Mode Decomposition Method for Data Analysis

TL;DR

The paper introduces the Extreme-Point Symmetric Mode Decomposition (ESMD) method, enhancing data analysis by improving the Hilbert-Huang Transform with adaptive decomposition and direct frequency-amplitude computation.

Contribution

The novel ESMD method employs multiple inner interpolating curves and an extreme-point symmetry approach for improved adaptive data decomposition and instantaneous frequency analysis.

Findings

Provides an adaptive global mean curve determination.

Enables direct computation of instantaneous frequency and amplitude.

Improves data analysis in various scientific fields.

Abstract

An extreme-point symmetric mode decomposition (ESMD) method is proposed to improve the Hilbert-Huang Transform (HHT) through the following prospects: (1) The sifting process is implemented by the aid of 1, 2, 3 or more inner interpolating curves, which classifies the methods into ESMD_I, ESMD_II, ESMD_III, and so on; (2) The last residual is defined as an optimal curve possessing a certain number of extreme points, instead of general trend with at most one extreme point, which allows the optimal sifting times and decompositions; (3) The extreme-point symmetry is applied instead of the envelop symmetry; (4) The data-based direct interpolating approach is developed to compute the instantaneous frequency and amplitude. One advantage of the ESMD method is to determine an optimal global mean curve in an adaptive way which is better than the common least-square method and running-mean approach; another one is to determine the instantaneous frequency and amplitude in a direct way which is better than the Hilbert-spectrum method. These will improve the adaptive analysis of the data from atmospheric and oceanic sciences, informatics, economics, ecology, medicine, seismology, and so on.