Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information

摘要

Permutation entropy (PE) has been recently suggested as a novel measure to characterize the complexity ofnnonlinear time series. In this paper, we propose a simple method to address some of PE’s limitations, mainly itsninability to differentiate between distinct patterns of a certain motif and the sensitivity of patterns close to thennoise floor. The method relies on the fact that patterns may be too disparate in amplitudes and variances andnproceeds by assigning weights for each extracted vector when computing the relative frequencies associated withnevery motif. Simulations were conducted over synthetic and real data for a weighting scheme inspired by thenvariance of each pattern. Results show better robustness and stability in the presence of higher levels of noise, innaddition to a distinctive ability to extract complexity information from data with spiky features or having abruptnchanges in magnitude.