Change Point and Anomaly-Aware QoT Forecasting Framework for Proactive Network Management

Reliable forecasting of optical performance monitoring (OPM) parameters under non-stationary conditions is a key enabler of proactive optical network management. In this letter, we propose a forecasting enhancement framework that integrates change point and collective anomaly detection scores as auxiliary features into a sequence-to-sequence QoT forecaster. Results on production OPM data show consistent improvements in forecasting accuracy over baseline models that do not integrate the auxiliary features. As a result, the proposed framework provides a consistently positive reaction window prior to QoT violations, with observed durations ranging from 1 to 10 days across failures, enabling proactive network operation without introducing additional control loop complexity.