Modelling Saudi Arabia's Crude Oil Production with ARIMA and ETS Time Series Methods

Abstract

This study analysed and forecasted Saudi Arabia’s annual crude oil production using time series models such as ARIMA and ETS. The research applied both models with ARIMA after pre-processing the data. It was selected based on the autocorrelation analysis and ETS with an additive and damped trend. Comparing models with the help of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) revealed that the ETS model gives a better fit due to the lower AIC and BIC values. Therefore, the ETS model is suggested for short-term predictions of crude oil production. The study highlights the significance of regularly assessing the model’s performance and considering exterior factors that are distressing production trends, which assists policymakers and stakeholders in strategic planning.