THE STUDY OF CORONAL MASS EJECTION CYCLES USING THE AUTOREGRESSIVE MODELING

Abstract

The Sun produces a fair amount of solar energy caused by the
different solar activities that affect the terrestrial environment.
The lowest part of the sun's atmosphere is photosphere gases
that can be recognized by the disk of the sun and above parts are
the chromospheres and corona. The three parts of the structure
of the sun's atmosphere crucial to understand the nature of
solar activities. The analysis of the study segregated monthly
cyclic data of green line emission corona 530.03λ nanometers
by appropriate stochastic models. After identifying stationary of
each cycle along with total data is observed and generate
autoregressive AR (p), MA (q) and ARIMA (p, d, q) models have
revealed a linear difference and ARMA for a total span based on
minimization of Akaike information criterion and Schwarz
Beysian criterion. The parameters are examined by ACF and the
PACF for different cyclic duration of Corona from 1944 to 2008.
The models (0,1,1), (1,1,1) and (2,1,0) for cycles and (1,0,1) for
total time series data were found the significance. The models
obtained in this paper may be useful to understand and forecast
the Coronal time series data.