anomaly-detection-time-series

Formal time-series methods that augment the hand-coded fingerprint library in traffic-change-diagnosis. Use this skill when traffic-change-diagnosis fingerprints overlap, when the user asks "is this real?", or when the change date is contested. Applies STL decomposition, Bayesian online changepoint detection, Prophet, quantile regression, sequential probability ratio test, and Granger causality. Use whenever interpreting a series where day-of-week confounds an eyeballed drop, where two candidate causes share a week, or where an alert needs to fire before an analyst sees the chart. Pairs with analytics-diagnostic-method for the surrounding investigation and with sequential-monitoring for the SPRT details. Triggers when Clamp MCP traffic_timeseries returns a series spanning more than 14 days, or when via Clamp the user shares a daily/hourly metric history that needs a non-eyeball verdict.

适合你，如果经常需要判断业务指标变化是真实异常还是偶然波动。