Long-Term QoT Forecasting in Dynamic Optical Networks via Decomposition-Driven Parallel Temporal Modeling

Accurate long-term forecasting of Quality of Transmission (QoT) is critical for the proactive operation and condition-aware management of dynamic elastic optical networks. However, the evolution of QoT is governed by multi-scale dynamics, including slow equipment aging, periodic operating variations, and short-term channel fluctuations, which a single temporal model struggles to capture jointly. To address this issue, we propose PA-TCN-Informer, a decomposition-driven parallel forecasting framework for long-horizon QoT prediction. The proposed framework first applies Seasonal-Trend decomposition using Loess (STL) to separate the Q-factor sequence into trend, seasonal, and residual components, and then employs Variational Mode Decomposition (VMD) to further resolve the residual into short-term fluctuation modes. The decomposed components, together with physical-layer monitoring features, are fed into a parallel TCN–Informer architecture, in which the TCN branch captures local temporal patterns while the Informer branch models long-range dependencies; the two streams are subsequently fused. We evaluate the proposed framework through Optuna-based hyperparameter optimization, STL/VMD sensitivity analysis, decomposition-method comparison, multi-seed baseline comparison with statistical testing, and zero-shot leave-one-dataset-out cross-domain evaluation. On the primary dataset, PA-TCN-Informer achieves the best overall forecasting accuracy among the compared models and reduces MAE by 2.2% relative to the serial TCN–Informer. In addition, the staged STL-VMD preprocessing alone yields a 60.8% reduction in MAE compared with raw inputs, confirming the value of physically interpretable multi-scale decomposition. In the zero-shot cross-domain setting, PA-TCN-Informer remains competitive across target domains. These results demonstrate that the proposed framework provides an effective and interpretable approach to QoT forecasting, and they further indicate that topology-aware modeling is a promising direction for improving cross-domain generalization.