Applying Forecasting Algorithms to Improve Budget Preparation Procedures in Government Units

It is necessary to use contemporary techniques to anticipate government budget items in light of the financial difficulties that governments face, especially scarce resources and growing spending demands. Using forecasting algorithms as a sophisticated technique to estimate government units' expenditures and revenues for the upcoming fiscal years. The study’s problem is that the traditional technique of producing the government budget confronts numerous problems, the most serious of which is the reliance on human estimates to determine the demands for various types of spending, resulting in a waste of public funds. Furthermore. The study's goal is to forecast future general budget expenditures, improve decision-making efficiency, and ensure accurate financial reporting by using accounting models supported by intelligent systems capable of continuous learning and development. In this study, the ARIMA statistical forecasting model was utilized to forecast budget items for 2021 based on historical data. The ARIMA statistical forecasting model performed well in forecasting budget items related to expenditures for the year 2021, with an amount of (177,165,336,299) and a slight downward trend, and the expected values were very close to the actual values, which were (179,228,498,212). Among the researcher's most important conclusions: forecasting algorithms represent a rational scientific method due to their ease of application and speed of data processing , while the most important recommendations is to use forecasting algorithms in the government budget preparation process, given their ability to handle massive amounts of data while producing results quickly and at the lowest possible cost.