arXiv:2512.08879v1 Announce Type: new 
Abstract: Real-world datasets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significantly diminish a model's predictive accuracy. Furthermore, the presence of hyperparameters in online models exacerbates this issue. These parameters are typically fixed and cannot be dynamically adjusted by the user in response to the evolving data distribution. Gaussian Process (GP) models offer powerful non-parametric regression capabilities with uncertainty quantification, making them ideal for modeling complex data relationships in an online setting. However, conventional online GP methods face several critical limitations, including a lack of drift-awareness, reliance on fixed hyperparameters, vulnerability to data snooping, absence of a principled decay mechanism, and memory inefficiencies. In response, we propose DAO-GP (Drift-Aware Online Gaussian Process), a novel, fully adaptive, hyperparameter-free, decayed, and sparse non-linear regression model. DAO-GP features a built-in drift detection and adaptation mechanism that dynamically adjusts model behavior based on the severity of drift. Extensive empirical evaluations confirm DAO-GP's robustness across stationary conditions, diverse drift types (abrupt, incremental, gradual), and varied data characteristics. Analyses demonstrate its dynamic adaptation, efficient in-memory and decay-based management, and evolving inducing points. Compared with state-of-the-art parametric and non-parametric models, DAO-GP consistently achieves superior or competitive performance, establishing it as a drift-resilient solution for online non-linear regression.

تم اقتراح طريقة جديدة تُدعى DAO-GP (عملية غاوسية على الإنترنت مع الوعي بالانحراف) لمعالجة التحديات التي تطرحها ظاهرة انحراف المفهوم في مجموعات البيانات الواقعية، والتي يمكن أن تؤثر بشكل كبير على دقة التنبؤ. تعزز هذه الطريقة المبتكرة نماذج عملية غاوسية من خلال السماح بإجراء تعديلات ديناميكية على المعلمات الفائقة استجابةً لتطور توزيعات البيانات، مما يحسن أداء النموذج في البيئات عبر الإنترنت.

Se ha propuesto un nuevo método llamado DAO-GP (Drift-Aware Online Gaussian Process) para abordar los desafíos que plantea la deriva de concepto en conjuntos de datos del mundo real, que pueden afectar significativamente la precisión predictiva. Este enfoque innovador mejora los modelos de Proceso Gaussiano al permitir ajustes dinámicos de los hiperparámetros en respuesta a la evolución de las distribuciones de datos, mejorando así el rendimiento del modelo en entornos en línea.

Une nouvelle méthode appelée DAO-GP (Drift-Aware Online Gaussian Process) a été proposée pour relever les défis posés par le concept de dérive dans les ensembles de données du monde réel, qui peuvent avoir un impact significatif sur la précision prédictive. Cette approche innovante améliore les modèles de processus gaussiens en permettant des ajustements dynamiques des hyperparamètres en réponse à l'évolution des distributions de données, améliorant ainsi les performances du modèle dans des contextes en ligne.

A new method called DAO-GP (Drift-Aware Online Gaussian Process) has been proposed to address the challenges posed by concept drift in real-world datasets, which can significantly impact predictive accuracy. This innovative approach enhances Gaussian Process models by allowing dynamic adjustments to hyperparameters in response to evolving data distributions, thereby improving model performance in online settings.

DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process

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