TY - GEN
T1 - Predicting Revenues and Expenditures Using Artificial Neural Network and Autoregressive Integrated Moving Average
AU - Alam, Teg
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/9
Y1 - 2020/9/9
N2 - The Saudi government has now set up several strategic strategies to predict the country's future, such as Saudi Vision 2030. Mathematical model and forecasting methods are significant instruments to achieve superior development in the country's economy. In this research, the Kingdom of Saudi Arabia's revenues and expenditures are predicted using models of the Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA). This article uses statistical software to forecast a time series data using ANN and ARIMA models on Kingdom of Saudi Arabia's total revenues and expenses from 1969 to 2018. The models ANN and ARIMA (1, 0, 0), ARIMA (0, 1, 1) and ARIMA (1,1,2) are found to be suitable for predicting the full revenue and expenditure of the Kingdom of Saudi Arabia.
AB - The Saudi government has now set up several strategic strategies to predict the country's future, such as Saudi Vision 2030. Mathematical model and forecasting methods are significant instruments to achieve superior development in the country's economy. In this research, the Kingdom of Saudi Arabia's revenues and expenditures are predicted using models of the Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA). This article uses statistical software to forecast a time series data using ANN and ARIMA models on Kingdom of Saudi Arabia's total revenues and expenses from 1969 to 2018. The models ANN and ARIMA (1, 0, 0), ARIMA (0, 1, 1) and ARIMA (1,1,2) are found to be suitable for predicting the full revenue and expenditure of the Kingdom of Saudi Arabia.
KW - Artificial Neural Networks (ANN)
KW - Autoregressive Integrated Moving Average (ARIMA)
KW - Exports
KW - Forecasting
KW - Imports and Kingdom of Saudi Arabia
UR - http://www.scopus.com/inward/record.url?scp=85098457585&partnerID=8YFLogxK
U2 - 10.1109/ICCIT-144147971.2020.9213814
DO - 10.1109/ICCIT-144147971.2020.9213814
M3 - Conference contribution
AN - SCOPUS:85098457585
T3 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
BT - 2020 International Conference on Computing and Information Technology, ICCIT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing and Information Technology, ICCIT 2020
Y2 - 9 September 2020 through 10 September 2020
ER -