Outlier detection

Monitors produce aggregate numbers over time: counts, rates, sums, and similar summaries per time window. Outlier detection flags unusual points—values that do not match the pattern of earlier monitor readings.

The shaded band is the "normal" range learned from history; the dashed line is its center. A point that lands far outside the band is unlikely under that history, so it is flagged.

The chart shows the idea at a glance: AIMO learns a band of normal from a monitor's past values, then judges each new reading against it. Points inside the band are expected; the red point is far less likely than history suggests, so it surfaces as an alert. The band is learned, not fixed—it widens or shifts as the series itself changes.

Conceptually:

1. A model of “normal” is inferred from past monitor values for that series—roughly, which values are common vs rare at a given moment (for example as a probability density over possible values).

2. Each new point is scored by how likely it would be under that historical picture. Unlikely points can drive alerts or review in the product.

The goal is to catch shifts and anomalies in monitor metrics after enough history exists to learn what “typical” means for your table.

Violations and stable baselines

If monitors count violations per window, outliers reflect changes in those counts—not a requirement that counts be zero. Stable high violation rates adapt as the baseline. See Monitors for how aggregates are produced.

Modeling runs on aggregates and artifacts in AIMO after the agent has supplied monitor time series. Raw rows stay on your side. For what the agent computes first, see Operations.