A Comparative Study of CO2 Emission Forecasting in the Gulf Countries Using Autoregressive Integrated Moving Average, Artificial Neural Network, and Holt-Winters Exponential Smoothing Models

Forecasting is the process of making predictions based on past and present data, with most common method being trend analysis. models are becoming increasingly crucial in uncovering intricate linkages between large amounts imprecise data uncontrollable variables. The main purpose this article to compare CO2 emission forecasts Gulf countries. In study, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), holt-Winters exponential smoothing (HWES) forecasting used anticipate emissions countries an annual basis. This study attempts predict time series using statistical tools. current analysis relied secondary gathered from United States Energy Information Administration (EIA). study’s findings show that ARIMA (1,1,1), Holt-Winters smoothing, (1,1,2), (2,1,2) do not outperform model estimating gives information state forecasts. will aid researcher’s understanding addition, government agencies can use develop strategic plans.