An Approach to Study on MA, ES, AR for Sunspot Number (SN) Prediction and to Forecast SN with Seasonal Variations Along with Trend Component of Time Series Analysis Using Moving Average (MA) and Exponential Smoothing (ES)

A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

“…The additive model ought to be considered when the seasonal variations are stable over time, while the multiplicative model is used when the seasonal variations are changing proportional to the level of the time series. Due to the variability of the amplitude of sunspot cycles, following Tabassum, Rabbani, and Omar (2020), we use the multiplicative model for the sunspot number prediction.…”

“…Although there have been many studies on predicting the sunspot number by using non-deep learning (Xu et al 2008;Hiremath 2008;Chattopadhyay, Jhajharia, and Chattopadhyay 2011;Tabassum, Rabbani, and Omar 2020) or deep learning forecasting methods (Pala and Atici 2019;Benson et al 2020;Arfianti et al 2021;Prasad et al 2022), most are based on ARMA models or deep learning methods like LSTM or GRU. Little work has been done on the more recent time-series models Prophet, Transformer, and Informer for the sunspot number prediction.…”

Sunspot activity prediction based on adaptive hybrid algorithms

“…In particular, an integrated model called XGBoost-dl is proposed which uses XGBoost as a two-level nonlinear integration method to combine deep learning models. [5] have estimated the sunspot number (SN) predictions over the recent solar cycle 24. To find the best model, moving average (MA), exponential smoothing (ES) and autoregression (AR) were used.…”