Algorithm for Forecasting The Nature of Public Services and Their Market Demand Dynamics

Abstract

Background: The demand for public services—such as healthcare, education, utilities, and social welfare—fluctuates over time due to demographic, economic, technological, and policy changes. Understanding and forecasting these dynamics is critical for effective public sector planning and resource allocation. Traditional forecasting models often fall short in addressing the complex and evolving nature of service needs in a market-influenced environment. Objective: This study aims to develop a predictive algorithm capable of analyzing and forecasting both the qualitative nature and quantitative market demand dynamics of public services. The goal is to provide decision-makers with a data-driven tool to anticipate service needs, optimize delivery, and respond proactively to emerging social and economic trends. Methods: A hybrid forecasting methodology was employed, integrating time series analysis, machine learning (ML) algorithms, and socio-economic indicators. Historical data on public service usage and demographic trends were collected from national statistical agencies and open government platforms. The core of the model includes autoregressive integrated moving average (ARIMA) models for baseline forecasting, enhanced with supervised learning techniques such as Random Forest and Gradient Boosting for feature importance analysis. The algorithm was tested and validated using real-world case studies from the healthcare and education sectors. Results: The proposed algorithm demonstrated high accuracy in forecasting service demand patterns over 1–5-year horizons. It successfully identified non-linear relationships between influencing factors such as population age, urbanization rates, income levels, and digital access. The results indicated that demand for digital public services is growing rapidly, while demand for traditional in-person services remains stable in rural areas. Feature analysis revealed that policy changes and economic shifts were the most significant drivers of demand volatility. Conclusion: The development of an intelligent algorithm for forecasting public service dynamics provides a valuable framework for improving service delivery and strategic public management. The integration of machine learning with traditional econometric methods enhances forecasting precision and adaptability. Governments and local authorities can leverage this tool to align resource distribution with future needs, promote service equity, and support evidence-based policy design. Further research is recommended to customize the model for specific service domains and regional conditions.