Sunspot numbers: Data analysis, predictions and economic impacts

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. © 2015 Kavala Institute of Technology.