Pinkness of the North Atlantic Oscillation signal revisited

Fernández, Irene Mirón; Pacheco, José Manuel Vilar; Quintana, María P.

doi:10.1016/j.physa.2010.08.003

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…9, the R MP of the NAO time series is consistent with those of the surrogates for all embedding dimensions. Hence, the null hypothesis cannot be rejected for all embedding dimensions tested, supporting the conclusion that the data are consistent with a linear stochastic system and in agreement with the literature [26,[28][29][30][31][32].…”

Section: A North Atlantic Oscillation Indexsupporting

confidence: 88%

Using missing ordinal patterns to detect nonlinearity in time series data

et al. 2017

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The number of missing ordinal patterns (NMP) is the number of ordinal patterns that do not appear in a series after it has been symbolized using the Bandt and Pompe methodology. In this paper, the NMP is demonstrated as a test for nonlinearity using a surrogate framework in order to see if the NMP for a series is statistically different from the NMP of iterative amplitude adjusted Fourier transform (IAAFT) surrogates. It is found that the NMP works well as a test statistic for nonlinearity, even in the cases of very short time series. Both model and experimental time series are used to demonstrate the efficacy of the NMP as a test for nonlinearity.

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Section: A North Atlantic Oscillation Indexsupporting

confidence: 88%

Using missing ordinal patterns to detect nonlinearity in time series data

et al. 2017

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“…We analyzed the monthly mean North Atlantic Oscillation (NAO) Index from January 1950 to December 2018. The NAO Index is the difference in atmospheric pressure at sea level between the Azores high and the Icelandic low, and has been shown by several groups of researchers, employing a range of techniques, to be stochastic 25,[91][92][93][94][95][96] . Data were downloaded from the Climate Prediction Center website (http://www.cpc.ncep.noaa.gov/).…”

Section: Datamentioning

confidence: 99%

A simple method for detecting chaos in nature

2020

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Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist’s toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.

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“…The PST concludes in favor of a stochastic dynamics for modeling this climatic index in agreement with the finding obtained by other authors. 43,[45][46][47][48][49] Finally, as the third and last real-world application, we have studied the historical temporal daily price evolution of two relevant commodities, namely, crude oil and gold. The former is the major energy source for the present economic activity, and the latter one is extremely popular for investment purposes.…”

Section: Real-world Applicationsmentioning

confidence: 99%

Discriminating chaotic and stochastic dynamics through the permutation spectrum test

Kulp

Zunino

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In this paper, we propose a new heuristic symbolic tool for unveiling chaotic and stochastic dynamics: the permutation spectrum test. Several numerical examples allow us to confirm the usefulness of the introduced methodology. Indeed, we show that it is robust in situations in which other techniques fail (intermittent chaos, hyperchaotic dynamics, stochastic linear and nonlinear correlated dynamics, and deterministic non-chaotic noise-driven dynamics). We illustrate the applicability and reliability of this pragmatic method by examining real complex time series from diverse scientific fields. Taking into account that the proposed test has the advantages of being conceptually simple and computationally fast, we think that it can be of practical utility as an alternative test for determinism. The importance of distinguishing between periodic, chaotic, and stochastic dynamics from time series analysis is well-recognized for understanding the mechanisms that govern the regarded complex systems. In this work, we have introduced a conceptually simple and computationally fast symbolic visual test for discriminating chaotic and stochastic dynamics, called the permutation spectrum test. Because the symbolization is made by implementing the Bandt and Pompe methodology, all the advantages associated with this natural encoding (simplicity, extremely fast calculation, robustness, and invariance with respect to monotonous transformations) are inherited by the permutation spectrum test. We have shown that this pragmatic approach is robust in situations in which other tests fail. We have also confirmed its practical utility by examining several experimental and natural time series.

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Pinkness of the North Atlantic Oscillation signal revisited

Cited by 5 publications

References 33 publications

Using missing ordinal patterns to detect nonlinearity in time series data

Using missing ordinal patterns to detect nonlinearity in time series data

A simple method for detecting chaos in nature

Discriminating chaotic and stochastic dynamics through the permutation spectrum test

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