The Amigó paradigm of forbidden/missing patterns: a detailed analysis

Rosso, Osvaldo A.; Carpi, Laura C.; Saco, Patricia M.; Ravetti, Martı́n Gómez; Larrondo, H.A.; Plastino, A.

doi:10.1140/epjb/e2012-30307-8

Cited by 33 publications

(28 citation statements)

References 51 publications

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“…Symbolic analysis is powerful to detect nontrivial hidden correlations in data. As shown by Rosso et al (2012); Carpi et al (2010) a correlated structure as produced by fractional Gaussian noise processes generates an uneven presence of patterns. Provided a sufficiently long time series, no pattern is forbidden.…”

Section: Simulation Of Fractional Brownian Motionmentioning

confidence: 99%

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Spurious Seasonality Detection: A Non-Parametric Test Proposal

2018

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This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called "day-of-the-week" effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies.

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Section: Simulation Of Fractional Brownian Motionmentioning

confidence: 99%

“…Another factor that influences results is the time series length. As recalled by Rosso et al (2012), short time series could result in the incorrect detection of forbidden patterns. In the Supplementary Material file we present the simulation and test of hypotheses for shorter time series.…”

Section: Empirical Applicationmentioning

confidence: 99%

Spurious Seasonality Detection: A Non-Parametric Test Proposal

2018

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“…We note that all q-complexity-entropy curves are closed, indicating that time series of length 2 17 of the fractional Brownian motion displays all possible permutations π j for d = 3 and d = 4. The presence of forbidden ordinal patterns in the fractional Brownian motion was studied by Rosso et al [35,36] and Carpi et al [37], where they observed that the number of forbidden ordinal patterns decreases with the time series length with a rate that depends on the Hurst exponent h. In particular, Carpi et al [37] showed that for d = 4 and very small time series (around one hundred steps), the fractional Brownian motion may have a few number of forbidden patterns; however, this number vanishes for series of length larger than 500 terms, which agrees with our findings. Also, for the fractional Brownian motion is possible to obtain the exact expression for the q-complexity-entropy curves, because Bandt and Shiha [38] have calculated the exact form of the probability distribution P = {p j (π j )} j=1,...,d!…”

Section: A Fractional Brownian Motionmentioning

confidence: 99%

Characterizing time series via complexity-entropy curves

et al. 2017

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The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q-complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

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“…In the case of H × C the variation range is [0, 1] × [C min , C max ] (with C min and C max the minimum and maximum statistical complexity values, respectively, for a given H S value [49]), while in the causality planes H × F and C × F the range is [0, 1] × [0, 1] in both cases. These causal information planes have been profitably used to separate and differentiate amongst chaotic and deterministic systems [50,34]; visualization and characterization of different dynamical regimes when the system parameters vary [32,33,34]; time dynamic evolution [57]; identifying periodicities in natural time series [58]; identification of deterministic dynamics contaminated with noise [59,60] and; estimating intrinsic time scales of delayed systems [54,55,56]; among other applications (see [6] and references therein).…”

Section: Causal Information Planesmentioning

confidence: 99%

Efficiency characterization of a large neuronal network: A causal information approach

Montani

Deleglise

Rosso

2014

Physica A: Statistical Mechanics and its Applications

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When inhibitory neurons constitute about 40% of neurons they could have an important antinociceptive role, as they would easily regulate the level of activity of other neurons. We consider a simple network of cortical spiking neurons with axonal conduction delays and spike timing dependent plasticity, representative of a cortical column or hypercolumn with large proportion of inhibitory neurons. Each neuron fires following a Hodgkin-Huxley like dynamics and it is interconnected randomly to other neurons. The network dynamics is investigated estimating Bandt and Pompe probability distribution function associated to the interspike intervals and taking different degrees of inter-connectivity across neurons. More specifically we take into account the fine temporal "structures" of the complex neuronal signals not just by using the probability distributions associated to the inter spike intervals, but instead considering much more subtle measures accounting for their causal information: the Shannon permutation entropy, Fisher permutation information and permutation statistical complexity. This allows us to investigate how the information of the system might saturate to a finite value as the degree of inter-connectivity across neurons grows, inferring the emergent dynamical properties of the system.

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The Amigó paradigm of forbidden/missing patterns: a detailed analysis

Cited by 33 publications

References 51 publications

Spurious Seasonality Detection: A Non-Parametric Test Proposal

Spurious Seasonality Detection: A Non-Parametric Test Proposal

Characterizing time series via complexity-entropy curves

Efficiency characterization of a large neuronal network: A causal information approach

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