Topological and Self-Structured Approaches to Supervised Anomaly Detection in Econometrics
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i3.857Keywords:
Robust econometrics, Anomaly detection, Regime shifts, Representation learning, Reproducible pipelines, Algebraic Topology, Nonstationary Time Series, Supervised Anomaly Detection, Econometric Modeling, Stability-Regularized Learning, Distributional Shift, Econometría robusta, Detección de anomalías, Topología algebraica, Detección supervisada de anomalías, Modelización econométrica, Aprendizaje regularizado por estabilidad, Cambio de distribuciónAbstract
This article proposes a robust econometric framework for anomaly detection in nonstationary time series affected by noise, outliers, and regime shifts. The method combines windowed feature construction, supervised learning, and stability-oriented regularization, while enabling optional topological and structural diagnostics to corroborate detected transitions. A reproducible pipeline trains models, calibrates decision thresholds, and preserves artifacts for transparent validation, including metrics, figures, and segment-level summaries. Experiments show consistent discrimination, improved reliability under distributional change, and interpretable latent representations that support operational monitoring. The results demonstrate that integrating robustness principles with structured diagnostics yields actionable early-warning signals for complex dynamic systems in practice.
Spanish-language metadata / Metadatos en español
Título en español:
Enfoques topológicos y autoestructurados para la detección supervisada de anomalías en econometría
Resumen:
Este artículo propone un marco econométrico robusto para la detección de anomalías en series temporales no estacionarias afectadas por ruido, valores atípicos y cambios de régimen. El método combina la construcción de características por ventanas, el aprendizaje supervisado y la regularización orientada a la estabilidad, al tiempo que permite realizar diagnósticos topológicos y estructurales opcionales para corroborar las transiciones detectadas. Un proceso reproducible entrena modelos, calibra umbrales de decisión y conserva los datos para una validación transparente, incluyendo métricas, figuras y resúmenes a nivel de segmento. Los experimentos muestran una discriminación consistente, una mayor fiabilidad ante cambios en la distribución y representaciones latentes interpretables que facilitan la supervisión operativa. Los resultados demuestran que la integración de principios de robustez con diagnósticos estructurados permite obtener señales de alerta temprana útiles para sistemas dinámicos complejos en la práctica.
Palabras Claves:
Econometría robusta, Detección de anomalías, Cambios de régimen, Aprendizaje de representaciones, Flujos de trabajo reproducibles, Topología algebraica, Series temporales no estacionarias, Detección supervisada de anomalías, Modelización econométrica, Aprendizaje regularizado por estabilidad, Cambio de distribución
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