Hybrid Econometric and Machine Learning Approaches for Robust Anomaly Detection

Anomalies, outliers, structural breaks, and irregular observations are no longer peripheral disturbances in empirical research; they have become defining characteristics of modern datasets across finance, macroeconomics, public policy, healthcare, and IoT environments. Traditional econometric models grounded in assumptions of linearity, homogeneity, and distributional regularity are often fragile in the presence of contamination, high-leverage points, or regime shifts. At the same time, machine learning approaches offer powerful detection capabilities and scalability but may lack interpretability and inferential coherence. This chapter proposes a hybrid methodological framework that integrates robust econometric estimation, influence-based diagnostics, and machine-learning anomaly detection to improve resilience, interpretability, and decision relevance in anomaly-rich settings.