A Stochastic Prediction Model for the Sunspot Cycles |
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Authors: | A Sabarinath A K Anilkumar |
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Institution: | 1.Applied Mathematics Division,Vikram Sarabhai Space Centre,Thiruvananthapuram,India |
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Abstract: | A stochastic prediction model for the sunspot cycle is proposed. The prediction model is based on a modified binary mixture
of Laplace distribution functions and a moving-average model over the estimated model parameters. A six-parameter modified
binary mixture of Laplace distribution functions is used for the modeling of the shape of a generic sunspot cycle. The model
parameters are estimated for 23 sunspot cycles independently, and the primary prediction-model parameters are derived from
these estimated model parameters using a moving-average stochastic model. A correction factor (hump factor) is introduced
to make an initial prediction. The hump factor is computed for a given sunspot cycle as the ratio of the model estimated after
the completion of a sunspot cycle (post-facto model) and the prediction of the moving-average model. The hump factors can be applied one at a time over the moving-average
prediction model to get a final prediction of a sunspot cycle. The present model is used to predict the characteristics of
Sunspot Cycle 24. The methodology is validated using the previous Sunspot Cycles 21, 22, and 23, which shows the adequacy
and the applicability of the prediction model. The statistics of the variations of sunspot numbers at high solar activity
are used to provide the lower and upper bound for the predictions using the present model. |
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