An early prediction of 25th solar cycle using Hurst exponent

Singh, Ashok K.; Bhargawa, Asheesh

doi:10.1007/s10509-017-3180-2

Cited by 45 publications

(15 citation statements)

References 31 publications

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“…For the number of sunspots, which is a well-known long-memory noisy time series, . In this case the value of obtained ( ) confirms the results of Singh et al 38 where a Hurst exponent close to 1 was found. Regarding the five time series of RR-intervals of healthy subjects, our algorithm identifies stochasticity ( ) in all of them, which is consistent with findings of Ref.…”

Section: Resultssupporting

confidence: 89%

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Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Boaretto

Budzinski

Rossi

et al. 2021

Sci Rep

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Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, $$\alpha$$ α , that determines the strength of the correlation of the noise. To predict $$\alpha$$ α the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the $$\alpha$$ α value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same $$\alpha$$ α parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community.

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Section: Resultssupporting

confidence: 89%

“…Dataset E-IV: Three time-series of the sunspots numbers for the period of 1976–2013 38 , the daily sunspots numbers depicts a noisy “pseudo-sinusoidal” behavior. It is accepted that magnetic cycles in the Sun are generated by a solar dynamo produced through nonlinear interactions between solar plasmas and magnetic fields 54 , 55 .…”

Section: Datasetsmentioning

confidence: 99%

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Boaretto

Budzinski

Rossi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…(This resulted in so large random forecast errors that it is practically unsuitable for prediction.) A more refined approach is simplex projection analysis, recently applied by Singh and Bhargawa (2017) for the problem of solar cycle prediction. (See also Sarp et al 2018.)…”

Section: Nonlinear Methodsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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A review of solar cycle prediction methods and their performance is given, including early forecasts for Cycle 25. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle. Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. The choice of a good precursor often implies considerable physical insight: indeed, it has become increasingly clear that the transition from purely empirical precursors to model-based methods is continuous. Model-based approaches can be further divided into two groups: predictions based on surface flux transport models and on consistent dynamo models. The implicit assumption of precursor methods is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time, and therefore it lends itself to analysis and forecasting by time series methods. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. One method that has This article is a revised version of

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“…As of recently, the method of RRA is still being utilized in analyzing medical data [30], geological data [31][32][33], internet traffic data [34,35], and solar activity data [36]. Additionally, it is also utilized in the field of engineering [37].…”

Section: Rescaled Range Analysis (Rra)mentioning

confidence: 99%

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

et al. 2021

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The fractal properties of geomagnetic northward component data (H-component) in the equatorial region during various phases of solar activity over Southeast Asia were investigated and then quantified using the parameter of the Hurst exponent (H). This study began with the identification of existence of spectral peaks and scaling properties in international quiet day H-component data which were measured during three levels of solar activity: low, intermediate, and high. Then, various cases of quiet and disturbed days during different solar activity levels were analyzed using the method that performed the best in the preceding part. In all the years analyzed, multifractal scaling and spectral peaks exist, signifying that the data have fractal properties and that there are external factors driving the fluctuations of geomagnetic activity other than solar activity. The analysis of various cases of quiet and disturbed days generally showed that quiet days had anti-persistence tendencies (H < 0.5) while disturbed days had persistence tendencies (H > 0.5)—generally a higher level of Hurst exponent compared to quiet days. As for long-term quiet day H-component data, it had a Hurst exponent value that was near H ≃ 0.50, while the long-term disturbed day H-component data showed higher values than that of the quiet day.

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An early prediction of 25th solar cycle using Hurst exponent

Cited by 45 publications

References 31 publications

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks

Solar cycle prediction

Identification of Fractal Properties in Geomagnetic Data of Southeast Asian Region during Various Solar Activity Levels

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