An Evaluation of Intrinsic Mode Function Characteristic of Non-Gaussian Autorregresive Processes

POSE, FERNANDO; JAVIER ZELECHOWER; MARCELO RISK; FRANCISCO REDELICO

Communications in Computer and Information Science

Springer Nature Switzerland A

Año: 2021; p. 111 - 124

Empirical mode decomposition (EMD) is a suitable transformation to analyse non-linear time series. This work presents a empirical study of intrinsic mode functions (IMFs) provided by the empiricalmode decomposition. We simulate several non-gaussian autoregressiveprocesses to characterize this decomposition. Firstly, we studied the probability density distribution, Fourier spectra and the cumulative relativeenergy to each IMF as part of the study of empirical mode decomposition. Then, we analyze the capacity of EMD to characterize, both theautocorrelation dynamics and the marginal distribution of each simulated stochastic process. Results show that EMD seems not to only discriminate autocorrelation but also the marginal distribution of simulatedprocesses. Results also show that entropy based EMD is a promising estimator as it is capable to distinguish between correlation and probabilitydistribution. However, the EMD entropy does not reach its maximumvalue in stochastic processes with uniform probability distribution.