Effective Machine Learning Based Hyperion Model is Used to Forecast Budget Accounting Systems by Incorporating the Role of Dimension

Abstract

Business dimensions, which encompass the model's business-specific objects, include personnel, regions, consumers, and products. To construct these dimensions and members, the Performance Management Architect is employed. When the Essbase outlines are deployed, the Profitability and Cost Management application generates the business dimensions as basic or generic dimensions without a type. Profitability and Cost Management can use dimension members and hierarchies created for other programs, such as Oracle Hyperion Planning, with this feature. Budgets for programming and responsibilities are typical in business planning. The annual program budget is assessed by executives in order to determine the company's revenue and expenditure projections. The paper explores and expands upon a subject of significant interest, which is pertinent to the current socio-economic context of the field. Its objective is to establish novel relationships of interdependence between social-economic factors and the evolution of income and expenditure.  This paper will examine and analyze various aspects of the evolution of income and expenditure, identifying measurable connections between socio-economic factors and the evolution of income and expenditure. It will also identify and articulate strong or weak relationships between cause (socio-economic factors) and effect (amount of income and expenses). The current research was initiated by the desire to model economic phenomena and provides more precise decision support through the application of contemporary analysis and prediction elements. Analyzing the appropriate regression methods in relation to the implementation of machine learning algorithms is a critical objective. The public budget is an excellent source of Big data for implementing a Machine Learning algorithm because it allows us to specify numerous dimensions for the same information. The conclusions and proposals that emerge from the examination of the causality and interdependence of the analyzed factors are intended to serve as a decisional support for state institutions and, at the same time, an element of comprehending and predicting economic phenomena.