TD_OutlierFilterFit
This function is used to identify outliers in a dataset. Outliers are values that differ significantly from other values in the dataset. Outlier’s can significantly skew statistical output and so disproportionately effect mathematical average and deviations. TD_OutlierFilterFit is used to identify outliers and apply some simple handling techniques to the output table produced by the function, namely delete, replace with null or use the median value for the group as a replacement value.
The example here creates an input table with 2 outlier values, amount = 12 and distance_km = 45. The table is provided as input to the function using the default ‘PERCENTILE’ as the outlier method (Carlings modification and Tukey’s fences can also be used) to apply to the target columns (amount, distance_km). The lower and upper bounds are set for the percentile, 5 and 95.
TD_OutlierFilterFit is useful in Teradata because it allows you to detect and handle outliers directly inside the database, avoiding the need to move large volumes of data to external analytics tools. This improves data quality before reporting, machine learning, and statistical analysis.
Documentation for the function is here TD_OutlierFilterFit • Database Engine 20 In-Database Analytic Functions • Reader • Teradata Developers Portal
