Abstract

Nowadays, Saudi government has established several strategic tactics such as Saudi Vision 2030 to predict the future of the country. In order to accomplish a superior growth in the economy of the country, mathematical model and forecasting techniques are important tools. In this study, total annual exports and imports of the Kingdom of Saudi Arabia are forecasted using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models. This paper tries to predict a time series data using ANN and ARIMA models on total annual exports and imports of Kingdom of Saudi Arabia from the year 1968 to the year 2017 with the help of statistical software XLSTAT. The applied models are used to predict some future values of total annual exports and imports of the Kingdom of Saudi Arabia. It is found that the ANN and ARIMA (1, 1, 2) and ARIMA (0, 1, 1) models are suitable for predicting the total annual exports and imports of the Kingdom of Saudi Arabia.

Highlights

  • The Kingdom of Saudi Arabia preserves the largest amount of export of petroleum and it has the second-largest proven petroleum and the fifth-largest proven natural gas reserves in the world

  • The data regarding the total annual exports and imports of the Kngdom was collected from Saudi Arabian Monetary Authority (SAMA)

  • The information were on yearly basis and in Saudi Arabian Riyal (SAR) from years 1968 to 2017

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Summary

Introduction

The Kingdom of Saudi Arabia preserves the largest amount of export of petroleum and it has the second-largest proven petroleum and the fifth-largest proven natural gas reserves in the world. The economy of the country depends primarily on oil and gas products. Saudi Arabia exported SAR 611.48B and imported SAR491.43B in 2016, yielding a positive trade balance of SAR 119.29B. The growth domestic product (GDP) of Saudi Arabia was SAR 2423.40B and its GDP per capita was SAR 204.08K

Artificial Neural Network
Basic Model of Artificial Neural Network
Result and Findings
Models for exports
Prediction of the export
Prediction of Import
Conclusion
Full Text
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