A review of time series modelling over coronal mass ejections (CMEs)

The evolution of electromagnetic devices is occurring at an exponential rate and our dependency upon the electronic devices like satellites is increasing at an unprecedented rate as well. This means that if these devices fail, we have no idea of how we will restore them and the services they provide. One such force capable of destroying them is Coronal Mass Ejection. The cost of losing even one satellite is unthinkable and if a powerful CME hits directly, it will take down most of them. Thus NASA constantly monitors the sun's activities and tries to predict such occurrences so that orbits of satellites can be changed accordingly. We are using same data of the sun provided by NASA and presenting an online probabilistic model based on time series forecasting techniques to predict such event based on sun's current state. This paper discusses and performs a comparative study of DAR, VAR, Gaussian, Non-Gaussian and Beta models to present a single or group of models for this type of problem.