Question

A retail analytics team stores three years of daily sales history in a BigQuery table with columns sale_date (DATE), store_id (STRING), and revenue (FLOAT64). They want to generate a 30-day revenue forecast per store directly in BigQuery, using SQL only, and need the model to automatically handle seasonality, holidays, and trend without manual feature engineering. Which approach best meets these requirements?

Accepted Answer

Create a BigQuery ML model with model_type='ARIMA_PLUS', specifying time_series_timestamp_col='sale_date', time_series_data_col='revenue', and time_series_id_col='store_id', then call ML.FORECAST.

Answer

Create a BigQuery ML model with model_type='LINEAR_REG' using sale_date converted to an integer offset as the only feature, then predict future rows with ML.PREDICT.

Answer

Export the table to Vertex AI and train a custom TensorFlow LSTM model, then batch-predict and load results back into BigQuery.

Answer

Create a BigQuery ML model with model_type='KMEANS' on revenue and sale_date to cluster seasonal patterns, then extrapolate cluster centroids forward 30 days.