Question

A retail analytics team stores three years of daily sales records in BigQuery, including columns for date, store_id, product_category, and units_sold. They want to produce a 90-day forecast of units_sold per store and product category directly in BigQuery, without exporting data to another platform. The analysts are SQL-proficient but have no experience managing ML infrastructure. Which approach best meets these requirements?

Accepted Answer

Train a BigQuery ML ARIMA_PLUS model with TIME_SERIES_ID_COL set to the store and category, then call ML.FORECAST to generate the 90-day projection.

Answer

Train a BigQuery ML linear regression model using the date's ordinal value as the single feature and predict units_sold for future dates.

Answer

Export the sales data to Vertex AI and train a custom TensorFlow sequence model, then batch-predict the next 90 days.

Answer

Train a BigQuery ML k-means clustering model to group stores by sales pattern and use the cluster centroids as the forecast.