Question

A demand-planning team wants to use Databricks AutoML through the UI to forecast weekly product sales for the next 12 weeks. Their Delta table contains a 'sales_date' column, a numeric 'units_sold' column, and a 'store_id' column identifying 40 different stores, each with its own time series. Which configuration is required for the AutoML forecasting experiment to produce separate forecasts per store?

Accepted Answer

Set 'units_sold' as the prediction target, 'sales_date' as the time column, and specify 'store_id' as the identity (time series identifier) column, with a horizon of 12 and frequency of weeks.

Answer

Set 'units_sold' as the prediction target, 'sales_date' as the time column, and leave the identity column blank so AutoML automatically detects the 40 series.

Answer

Run 40 separate classification experiments, one per store, each filtered to a single 'store_id'.

Answer

Set 'store_id' as the prediction target and 'sales_date' as the time column, using a horizon of 12 weeks.