We compare trend-aware speculative alerting
against lagging threshold baselines in streaming telemetry. A
fast linear trend estimate projects a horizon H; if the projected
value exceeds a multiplicative bound, we alert early. We quantify
predictive F1, early-warning lead time, and false positives,
and show how RMSNorm-style running normalization improves
robustness across heterogeneous metric scales.