Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques

Abstract

Forecasting models are essential for economic development and making appropriate policy decisions. The purpose of this study is to forecast crude oil production in Saudi Arabia for the following year. Our study is aimed at predicting Saudi Arabia’s crude oil production using Artificial Neural Networks (ANN), Holt-Winters Exponential Smoothing (HW), and Autoregressive Integrated Moving Averages (ARIMA). Based on 1993-2022 crude oil production (million barrels per day) data, this study applies statistical analysis to forecast time series data based on said models over a period. The study also analyzes the forecast model’s accuracy using a variety of measures. As a result of the analysis, this study found that ANNs are the most effective at predicting crude oil production. Thus, among other models analyzed in this study, the ANN model can accurately predict Saudi Arabia’s crude oil production in the future. In addition, the study aims to clarify the current situation of crude oil production in the kingdom. Researchers will be able to better understand crude oil production forecasts as a result of this study. This study can also provide guidance for developing a strategic plan for government entities.