MACHINE LEARNING INTEGRATION WITH CLASSICAL STATISTICAL TECHNIQUES FOR IMPROVED FORECASTING ACCURACY

Abstract

Forecasting in macroeconomic and complex dynamic systems is fundamentally constrained by the coexistence of long-run trends, seasonal regularities, regime shifts, and nonlinear interdependencies. Classical statistical models provide interpretability, theoretical guarantees, and temporal discipline, yet they fail under nonstationarity and structural breaks. Machine learning models offer representational flexibility but often lack stability, transparency, and economic coherence. This paper proposes a diagnostically grounded hybrid forecasting framework that integrates classical statistical techniques with machine learning in a principled, structure-aware manner. Using a multivariate macroeconomic dataset, we first conduct comprehensive structural diagnostics, including stationarity testing, seasonality decomposition, lag dependency analysis, volatility assessment, and multicollinearity evaluation. These diagnostics reveal heterogeneous data-generating mechanisms across variables, rendering monolithic modeling strategies structurally misspecified. The proposed hybrid architecture assigns complementary roles to each paradigm: statistical models extract interpretable structural components, while machine learning models capture nonlinear interactions, regime sensitivity, and residual dynamics. Empirical results demonstrate that the hybrid framework consistently outperforms standalone classical and machine learning baselines in both predictive accuracy and robustness, particularly during periods of structural transition. Beyond performance gains, this study reframes forecasting as a structural inference problem rather than a purely algorithmic one, emphasizing the necessity of data–model compatibility for reliable long-horizon prediction.