Many researchers have been interested in improving known forecasting methods using several methods, including hybridizing time series models with one, two, or more models in order to obtain greater accuracy in predicting the data of the series to be predicted. Therefore, our research is an extension of the researchers who preceded us in order to provide forecasting methods that have an impact in the field in which they are used. Therefore, our study focused on the ARMAX model, as it is a model that combines time series models and a regression model, and it becomes more effective when it is hybridized with one of the artificial intelligence models (ANN) in order to improve forecasting results, which are considered important in decision-making and in all areas of life, including the average temperature in Basra Governorate. A comparison was made between the ARMAX models, the ANN model, and the ARMAX-ANN hybrid model in order to predict temperatures for the years (2024-2028), as it became clear that the hybrid model is better at forecasting using the MSE criterion.
Keywords
ANN modelARMAX modelhybrid modelsMSE.
References
F. Muhammad and M. A. Fakhri, “Predict the values of the time series using the (ARMAX) model with practical application,” Journal of Economic and Administrative Sciences, vol. 22, no. 8, 2016.
M. A. Alkali et al., “Assessing the forecasting performance of ARIMA and ARIMAX models of residential prices in Abuja Nigeria,” Asia Proceedings of Social Sciences, vol. 4, no. 1, 2019.
S. A. Musa et al., “Arima and Arimax analysis on the effect of variability of rainfall, temperature, humidity on some selected crops in Nasarawa State,” International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 15, no. 9, 2021.
S. Lusiana et al., “Forecasting stock price using ARMAX-GARCHX model during the COVID-19 pandemic,” Mathline Journal: Matematika dan Pendidikan Matematika, vol. 8, no. 2.
M. A. M. Ahmed et al., “The role of artificial neural networks in predicting foreign exchange rates and their impact on the stock prices of banks listed on the Egyptian Stock Exchange,” Scientific Journal of Financial and Administrative Studies and Research, vol. 2024, 2024.
J. A. Al-Shawadfi and A.-W. A. Al-Sayed, “Artificial intelligence and time series analysis,” Scientific Journal of Faculties of the Business Sector, no. 10, 2013.
H. Alrweilia and H. Fawzy, “Forecasting crude oil prices using an ARIMA-ANN hybrid model,” Journal of Statistics Applications & Probability: An International Journal, vol. 11, no. 3, 2022.
M. H. M. Al-Eesa and R. A. Zalan, “Using the ARFIMA-RBFN hybrid model to forecast oil prices,” Journal of Advances in Nonlinear Variational Inequalities, vol. 28, no. 5, 2025.
L. Hang et al., “An ARIMA-ANN hybrid model for time series forecasting,” Systems Research and Behavioral Science, vol. 30, no. 3, 2013.
C. Li and W. Sun, “The study on electricity price forecasting method based on time series ARMAX model and chaotic particle swarm optimization,” International Journal of Advancements in Computing Technology (IJACT), vol. 4, pp. 198-205, 2012.
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