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

Ali Alarjani; Teg Alam; Ahmed Farouk Kineber

doi:10.1109/ISMODE56940.2022.10180990

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

Ali Alarjani
, Teg Alam
, Ahmed Farouk Kineber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - ISMODE 2022
Subtitle of host publication	2nd International Seminar on Machine Learning, Optimization, and Data Science
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	524-528
Number of pages	5
ISBN (Electronic)	9781665455640
DOIs	https://doi.org/10.1109/ISMODE56940.2022.10180990
State	Published - 2022
Event	2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022 - Virtual, Online, Indonesia Duration: 22 Dec 2022 → 23 Dec 2022

Publication series

Name	Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science

Conference

Conference	2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022
Country/Territory	Indonesia
City	Virtual, Online
Period	22/12/22 → 23/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

and auto-regressive integrated moving averages
artificial neural networks
crude-oil
Holt-Winters exponential smoothing
Prediction

Access to Document

10.1109/ISMODE56940.2022.10180990

Cite this

Alarjani, A., Alam, T., & Farouk Kineber, A. (2022). Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques. In Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science (pp. 524-528). (Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISMODE56940.2022.10180990

Alarjani, Ali ; Alam, Teg ; Farouk Kineber, Ahmed. / Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques. Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 524-528 (Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science).

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title = "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.",

keywords = "and auto-regressive integrated moving averages, artificial neural networks, crude-oil, Holt-Winters exponential smoothing, Prediction",

author = "Ali Alarjani and Teg Alam and \{Farouk Kineber\}, Ahmed",

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year = "2022",

doi = "10.1109/ISMODE56940.2022.10180990",

language = "English",

series = "Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science",

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Alarjani, A , Alam, T & Farouk Kineber, A 2022, Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques. in Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science. Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science, Institute of Electrical and Electronics Engineers Inc., pp. 524-528, 2nd International Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022, Virtual, Online, Indonesia, 22/12/22. https://doi.org/10.1109/ISMODE56940.2022.10180990

Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques. / Alarjani, Ali ; Alam, Teg; Farouk Kineber, Ahmed.
Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science. Institute of Electrical and Electronics Engineers Inc., 2022. p. 524-528 (Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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M3 - Conference contribution

AN - SCOPUS:85167448940

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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 -

Alarjani A , Alam T, Farouk Kineber A. Modeling and Predicting Saudi Crude Oil Production Using Artificial Neural Networks (ANN) and Some Others Predictive Techniques. In Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science. Institute of Electrical and Electronics Engineers Inc. 2022. p. 524-528. (Proceedings - ISMODE 2022: 2nd International Seminar on Machine Learning, Optimization, and Data Science). doi: 10.1109/ISMODE56940.2022.10180990

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

Abstract

Publication series

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UN SDGs

Keywords

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