Bridging Temporal Dynamics and Nonlinear Patterns inMacroeconomic Forecasting: A Hybrid Statistical Learning Approach

Accurate macroeconomic forecasting is essential for effective policy formulation, financial ‎planning, and economic stability, yet it remains challenging due to structural changes, economic ‎shocks, and complex nonlinear interactions among economic indicators. Traditional time series ‎models often struggle to capture such complexities, while standalone machine learning approaches ‎may overlook temporal dependencies. To address these limitations, this study proposes a Hybrid ‎Predictive Framework for Macroeconomic Forecasting (HPFMF) that integrates complementary ‎strengths of time series and machine learning models. Using annual macroeconomic data for more ‎than 200 countries spanning 2010–2025 from the World Bank Open Data API, the framework ‎applies systematic preprocessing, including missing value handling, winsorization, logarithmic ‎transformation, and feature scaling. A time series hybrid combining ARIMA, Prophet, and ‎Exponential Smoothing captures temporal dynamics and structural shifts, while a stacked machine ‎learning ensemble of Random Forest, XGBoost, and Support Vector Regression models nonlinear ‎interdependencies. These layers are integrated through validation-based weighting to generate ‎robust forecasts. Empirical results show that the proposed framework achieves superior ‎performance, with significant reductions in forecasting errors and an R² of 0.92. Country-wise and ‎temporal validations confirm strong generalizability, demonstrating the framework’s effectiveness ‎for reliable, policy-oriented macroeconomic forecasting‎.