Abstract
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 empirical mode decomposition. We simulate several non-gaussian autoregressive processes to characterize this decomposition. Firstly, we studied the probability density distribution, Fourier spectra and the cumulative relative energy to each IMF as part of the study of empirical mode decomposition. Then, we analyze the capacity of EMD to characterize, both the autocorrelation 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 simulated processes. Results also show that entropy based EMD is a promising estimator 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.
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Pose, F., Zelechower, J., Risk, M., Redelico, F. (2021). An Evaluation of Intrinsic Mode Function Characteristic of Non-Gaussian Autorregresive Processes. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_9
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