Patricia M. Saco | Searcharxiv

arXiv2011

Noisy-chaotic time series and the forbidden/missing patterns paradigm

We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative importance in a given time series. To this end we extend i) the use of ordinal patterns-based probability distribution functions associated with a time series [Bandt and Pompe, Phys. Rev. Lett. 88 (2002) 174102] and ii) the so-called Amigó paradigm of forbidden/missing patterns [Amigó, Zambrano, Sanjuán, Europhys. Lett. 79 (2007) 50001], to analyze deterministic finite time series contaminated with strong additive noises of different correlation-degree. Insights pertaining to the deterministic component of the original time series are obtained with the help of the causal entropy-complexity plane [Rosso et al. Phys. Rev. Lett. 99 (2007) 154102].

cond-mat.stat-mech↗

arXiv2011

Causality and the Entropy-Complexity Plane: Robustness and Missing Ordinal Patterns

We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, i) the "causal" entropy-complexity plane [Rosso et al. Phys. Rev. Lett. 99 (2007) 154102] and ii) the estimation of the decay rate of missing ordinal patterns [Amigó et al. Europhys. Lett. 79 (2007) 50001, and Carpi et al. Physica A 389 (2010) 2020-2029]. In this work we extend the use of these techniques to address the analysis of deterministic finite time series contaminated with additive noises of different degree of correlation. The chaotic series studied here was via the logistic map (r = 4) to which we added correlated noise (colored noise with f-k Power Spectrum, 0 {\leq} k {\leq} 2) of varying amplitudes. In such a fashion important insights pertaining to the deterministic component of the original time series can be gained. We find that in the entropy-complexity plane this goal can be achieved without additional computations.

cond-mat.stat-mech↗