AI-DRIVEN ANOMALY DETECTION, OUTAGE PREDICTION, AND SELF-HEALING IN TELECOM PROVISIONING SYSTEMS

Abstract

Artificial intelligence offers powerful mathematical tools for enhancing the reliability of telecom provisioning systems, where complex workflows and fluctuating operating conditions frequently give rise to anomalies, performance drift, and partial service degradation. A unified analytical framework is developed to address three critical reliability components: anomaly detection, outage prediction, and autonomous recovery. Anomaly detection is formulated through the geometry of isolation-based scoring, enabling the identification of irregular system states without prior labeling. Temporal degradation is captured using an autoregressive forecasting structure, where deviations between predicted and observed latency reveal early signs of instability and provide a quantitative basis for pre-outage warning signals. To restore degraded performance, a self-healing mechanism is modeled as a contracting transformation applied to the latency trajectory, gradually driving the system back toward a stable equilibrium. Numerical evaluations across all three components demonstrate the ability to detect emerging anomalies, anticipate the onset of critical degradation, and reduce latency following corrective intervention. The integration of geometric, statistical, and dynamical principles results in a mathematically grounded pipeline capable of supporting autonomous, data-driven assurance in next-generation telecom environments. The findings establish a foundation for advanced modeling approaches, including nonlinear dynamics, optimal control, and real-time adaptive decision systems, aimed at strengthening the resilience of complex communication infrastructures.