Eastern Macedonia and Thrace Institute of Technology
Abstract
We analyze the monthly sunspot number (SSN) data from January 1749 to June 2013. We use the Average Mutual Information
and the False Nearest Neighbors methods to estimate the suitable embedding parameters. We calculate the
correlation dimension to compute the dimension of the system’s attractor. The convergence of the correlation dimension
to its true value, the positive largest Lyapunov exponent and the Recurrence Quantitative Analysis results provide
evidences that the monthly SSN data exhibit deterministic chaotic behavior. The future prediction of monthly
SSN is examined by using a neural network-type core algorithm. We perform ex-post predictions comparing them
with the observed SSN values and the predictions published by the Solar Influences Data Analysis Center. It is shown
that our technique is a better candidate for the prediction of the maximum monthly SSN value. We perform future
predictions trying to forecast the maximum SSN value from July 2013 to June 2014. We show that the present cycle
24 is yet to peak. Finally, the negative economic impacts of maximum solar activity are discussed
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