Predictions of the maximum amplitude for solar cycle 23 and its subsequent behavior using nonlinear methods

Two nonlinear methods are employed for the prediction of the maximum amplitude for solar cycle 23 and its declining behavior. First, a new heuristic method based on the second derivative of the (conveniently smoothed) sunspot data is proposed. The curvature of the smoothed sunspot data at cycle minimum appears to correlate (R 0.92) with the cycle's later-occurring maximum amplitude. Secondly, in order to predict the near-maximum and declining activity of solar cycle 23, a neural network analysis of the annual mean sunspot time series is also performed. The results of the present study are then compared with some other recent predictions.     

Secular Behavior of Solar Magnetic Activity: Nonstationary Time-Series Analysis of the Sunspot Record

The sunspot record of solar magnetic activity is studied as a nonstationary time series by means of a previously developed algorithm for treating perturbed dynamical systems. This approach incorporates secular changes into the modeling process through an external driving parameter, whose temporal behavior is shown to correspond in this case to the long-term trend of the sunspot record. Our method is able to reduce by approximately 13% the prediction error of this series when compared to the standard stationary approach. Such a reduction is remarkable in view of the benchmark status of the sunspot record in the statistical literature and, moreover, the fact that this gain is obtained over the performance of an already very competitive modeling technique based on ensembles of artificial neural networks.