On a 57-day, ten-router Internet2 traffic corpus, dense MLP forecaster TiDE cuts baseline prediction error by 30 to 42 percent relative to SARIMA and XGBoost, while anomaly masking gives only small robustness gains.
A fluid-flow characterization of Internet1 and Internet2 traffic,
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Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining
On a 57-day, ten-router Internet2 traffic corpus, dense MLP forecaster TiDE cuts baseline prediction error by 30 to 42 percent relative to SARIMA and XGBoost, while anomaly masking gives only small robustness gains.