Sourcetable offers multiple anomaly detection methods. The AI selects the best approach based on your data and goal.Documentation Index
Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
Statistical methods
Z-score
IQR method
Modified Z-score (MAD)
Uses Median Absolute Deviation instead of standard deviation. Resistant to outliers in the reference data.ML methods
Isolation Forest
- High-dimensional data
- No need for labeled examples
- Mixed feature types
DBSCAN
- Spatial data
- Clusters of arbitrary shape
- Unknown number of anomalies
Local Outlier Factor (LOF)
Time series anomalies
- 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
Use cases
| Domain | What to detect | Suggested method |
|---|---|---|
| Finance | Fraudulent transactions | Isolation Forest, Z-score |
| Manufacturing | Defective products | LOF, IQR |
| IT operations | Server anomalies | Rolling statistics, DBSCAN |
| Healthcare | Abnormal lab results | Modified Z-score |
| E-commerce | Unusual order patterns | Isolation Forest |
| IoT | Sensor malfunctions | Time series methods |