Question

A retail analytics team uses BigQuery ML to forecast weekly sales. Business stakeholders want to understand how much of each forecast is driven by trend, weekly seasonality, and holiday effects so they can validate the model with domain knowledge. The team already has a trained ARIMA_PLUS model. Which approach lets them retrieve the decomposed forecast components alongside the point forecasts with the least effort?

Accepted Answer

Use ML.EXPLAIN_FORECAST on the model, which returns the forecast plus its decomposed time-series components such as trend, seasonality, and holiday effects.

Answer

Use ML.FORECAST and manually compute component contributions by differencing consecutive forecast values in a SQL query.

Answer

Export the ARIMA_PLUS model to Vertex AI and enable Vertex Explainable AI feature attributions to obtain the component breakdown.

Answer

Retrain the model with ENABLE_GLOBAL_EXPLAIN=TRUE and then call ML.GLOBAL_EXPLAIN to get the seasonal and trend contributions.