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

FERNANDO POSE; FRANCISCO REDELICO; MARCELO RISK; JAVIER ZELECHOWER

Communications in Computer and Information Science

Springer, Cham

Año: 2021; p. 111 - 124

Empirical mode decomposition (EMD) is a suitable trans- formation to analyse non-linear time series. This work presents a empir- ical study of intrinsic mode functions (IMFs) provided by the empirical mode decomposition. We simulate several non-gaussian autoregressive processes to characterize this decomposition. Firstly, we studied the prob- ability density distribution, Fourier spectra and the cumulative relative energy to each IMF as part of the study of empirical mode decomposi- tion. Then, we analyze the capacity of EMD to characterize, both the autocorrelation dynamics and the marginal distribution of each simu- lated stochastic process. Results show that EMD seems not to only dis- criminate autocorrelation but also the marginal distribution of simulated processes. Results also show that entropy based EMD is a promising esti- mator as it is capable to distinguish between correlation and probability distribution. However, the EMD entropy does not reach its maximum value in stochastic processes with uniform probability distribution.