2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 134-150, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Ahmed Ali 1 * , Atef Masmoudi 2
Doi: https://doi.org/10.54216/FPA.190211
The E-Government Development Index (EGDI) represents the performance and reality of e-government. The importance of maintaining and planning for the enhancement of such an index enables the policymakers to understand, process, and develop the right plans and strategies for it. In this paper, the Auto Regressive Integrated Moving Average (ARIMA) has been utilized to build predictive models. The time-series data collected from the UN survey versions for the years 2003, 2005, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, and 2024 for the countries of Iraq and KSA. The necessary data maintenance was implemented, then analyzed, covering the inspection of their temporal behavior. Afterwards, two individual data sets were created for both countries under study, containing 253 months. The optimal values for the ARIMA models were determined by implementing the data transformation, including the autocorrelation function (ACF) and partial autocorrelation function (PACF). 80% of the dataset is used for training, and 20% is used for testing. The data residuals analyzed by ACF, PACF, and the Ljung-Box test were performed for the residuals independence check. Nine metrics were utilized for model evaluation and ruthlessness. By using ARIMA models, the e-government performance (EGDI) has been predicted for the next five years for Iraq and KSA. The ARIMA models for both Iraq and KSA showed high performance, where the RMSE value for the Iraq model was (0.0054) and the MAE value was (0.0031) compared to the RMSE value (0.0481) and the MAE value (0.0093) for the KSA model. The Iraq arima model has better quality of the prediction in absolute terms. On the other hand, the ARIMA model for KSA was better in terms of predicted trends with an accuracy of 98.44% compared to 97.39% for the Iraq model.
ARIMA , E-Government , EGDI , Model Building , Time-Series ,
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