We compare trend-aware speculative alertingagainst lagging threshold baselines in streaming telemetry. Afast linear trend estimate projects a horizon H; if the projectedvalue exceeds a multiplicative bound, we alert early. We quantifypredictive F1, early-warning lead time, and false positives,and show how RMSNorm-style running normalization improvesrobustness across heterogeneous metric scales.