TY - GEN
T1 - Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques
AU - Alarjani, Ali
AU - Alam, Teg
AU - Farouk Kineber, Ahmed
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - and auto-regressive integrated moving averages
KW - artificial neural networks
KW - crude-oil
KW - Holt-Winters exponential smoothing
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85167448940&partnerID=8YFLogxK
U2 - 10.1109/ISMODE56940.2022.10180990
DO - 10.1109/ISMODE56940.2022.10180990
M3 - Conference contribution
AN - SCOPUS:85167448940
T3 - Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science
SP - 524
EP - 528
BT - Proceedings - ISMODE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022
Y2 - 22 December 2022 through 23 December 2022
ER -
