Abstract
The different cycles of solar activity defines space weather variation and future space variability. Such as Solar Corona produces energy in micro and nano flares into the space climate. In this regards the time series analysis of monthly solar Coronal index data (1944 to 2008) is used, which contains six cycles of different length and peaks. In this study different probability distributions like Johnson SB, Beta, Gen. Pareto, Gen. Gamma, Triangular, Error, Dagum and Fatigue Life are fitted on Coronal cycles. The significance probability distributions are obtained using Kolmogrov- Smirnove, Anderson Darling and Chi square statistical tests. The Johnson SB distribution is found best fitted on all solar activity cycles along with the total time series data by Kolmogrov Smirnove test. The Coronal index monthly data is generated from 2008 to 2016 using a Monte Carlo simulation technique. While two other tests show variation in the fitted probability distributions for all cycles. With the help of significant probability distribution the expected length and peak of next Coronal index cycle data can be obtained.
