Uncertainty-Aware QoS Forecasting with BR-LSTM for Esports Networks

Reliable forecasting of network QoS indicators such as latency, jitter, and packet loss is essential for managing real-time and risk-sensitive applications. This study addresses the challenge of uncertainty quantification in QoS prediction by proposing a Bayesian Regression-enhanced Long Short-Term Memory (BR-LSTM) framework. The method integrates Bayesian mean variance estimates into sequential LSTM learning to enable accurate point forecasts and well-calibrated confidence intervals. Experiments are conducted using a Mininet-based emulation platform that simulates dynamic esports network environments. The proposed model is benchmarked against ten probabilistic and deterministic baselines, including ARIMA, Gaussian Process Regression, Bayesian Neural Networks, and Monte Carlo Dropout LSTM. Results demonstrate that BR-LSTM achieves competitive accuracy while providing uncertainty intervals that improve decision confidence for Service-Level Agreement (SLA) management. The calibrated upper bound (μ+kσ) can be compared directly against SLA thresholds to issue early warnings and prioritize rerouting, pacing, or bitrate adjustments when the bound approaches or exceeds policy limits, while calibration controls false alarms and prevents unnecessary interventions. The findings highlight the potential of uncertainty-aware forecasting for intelligent information systems in latency-critical networks.