Multiresolution mode decomposition for adaptive time series analysis

This paper proposes the multiresolution mode decomposition (MMD) as a novel model for adaptive time series analysis. The main conceptual innovation is the introduction of the multiresolution intrinsic mode function (MIME) of the form Sigma(N/2-1)(n=-N/2) a(n) cos (2 pi n phi(t))s(cn) (2 pi N phi(t))+ Sigma(N/2-1)(n=-N/2 ) b(n )sin (2 pi n phi(t)) s(sn) (2 pi N phi(t)) to model nonlinear and non-stationary data with time-dependent amplitudes, frequencies, and waveforms. The multiresolution expansion coefficients {a(n)}, {b(n)}, and the shape function series {s(c)n (t)} and {s(sn) (t)} provide innovative features for adaptive time series analysis. For complex signals that are a superposition of several t ion MIMFs with well-differentiated phase functions phi(t), a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series. Numerical ion examples from synthetic data and natural phenomena are given to demonstrate the power of this new method. (C) 2019 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a novel model called multiresolution mode decomposition (MMD) for adaptive time series analysis. The multiresolution intrinsic mode function (MIME) is proposed to model nonlinear and non-stationary data, showcasing the method's effectiveness in real-world applications.