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Openlayer’s anomaly detection helps you spot unexpected changes in production data with minimal setup. It powers the Anomaly detection test and flags when metrics for a column deviate from their learned “normal” behavior.

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What it monitors

For each table you connect, Openlayer maintains time series for:
  • Numeric columns — the mean daily value over time.
  • Categorical columns — the counts of the daily top categories over time.
These series are learned per column and continuously updated as new data arrives.

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How detection works

Behind the scenes, what Openlayer is doing is:
1

Learn

We fit time-series models per monitored metric (numeric mean, categorical counts). The model captures trend/seasonality and estimates an expected range (upper & lower bounds) for each evaluation window.
2

Predict

For the current window, the model predicts the upper and lower bounds.
3

Compare

We compare the observed value with the predicted range. If the observed value is outside the predicted range, it is flagged as an anomaly.
4

Adapt

Models are retrained regularly so the expected range improves as your data evolves.

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Visualizing results

When an anomaly is detected, the result view shows:
  • A time-series chart with the observed value and the expected range band. Green points fall within the band; orange points indicate anomalies.
  • A per-column selector to inspect any monitored metric individually.
Time series

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Root-cause analysis (RCA)

For categorical anomalies, the Diagnostics tab includes RCA:
  • Pick one or more categorical columns.
  • See which values were predominant when the anomaly occurred.
  • Use this to quickly form hypotheses (e.g., “spike only for to_address = X”), trace pipeline issues, or decide whether to suppress a benign seasonal effect. Root-cause analysis

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Configuration tips

  • Confidence interval (interval_width) Smaller interval → more sensitive (more anomalies). Larger interval → less sensitive.
  • Timestamp column Required so Openlayer can order data by time and form windows.
  • Cold start New tables need some history to build a reliable baseline. Early bounds may be wider until the model learns seasonality.

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FAQ

A per-window value (numeric mean or categorical count) that falls outside the model’s predicted upper/lower bounds for that column.
Yes—both excursions above the upper bound and below the lower bound are flagged.
Yes. As history grows, bounds adapt to recurring patterns, reducing false positives.
Yes. Create an Anomaly detection test and configure notifications on failures.
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