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Sourcetable offers multiple anomaly detection methods. The AI selects the best approach based on your data and goal.

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Statistical methods

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Z-score

Identifies values more than N standard deviations from the mean. Works best with normally distributed data.

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IQR method

Flags values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR. More robust to non-normal distributions than Z-score.

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Modified Z-score (MAD)

Uses Median Absolute Deviation instead of standard deviation. Resistant to outliers in the reference data.

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ML methods

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Isolation Forest

Isolates anomalies by randomly partitioning the feature space. Works well with:
  • High-dimensional data
  • No need for labeled examples
  • Mixed feature types

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DBSCAN

Points that don’t belong to any cluster are flagged as anomalies. Good for:
  • Spatial data
  • Clusters of arbitrary shape
  • Unknown number of anomalies

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Local Outlier Factor (LOF)

Compares local density of each point to its neighbors. Catches anomalies that are normal globally but unusual locally.

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Time series anomalies

Methods:
  • Rolling statistics — flag values outside rolling mean ± N×rolling std
  • Seasonal decomposition — flag large residuals after removing trend and seasonality
  • Prophet-style — detect changepoints and outliers in seasonal time series

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Use cases