A data analyst at a retail company trained a time-series model named sales_data.daily_revenue_model using the ARIMA_PLUS model type in BigQuery ML. The business team needs to project future daily sales for the next 30 days with an 80% prediction interval. Which BigQuery ML SQL query should the analyst run to generate these future values?

SELECT * FROM ML.FORECAST(MODEL `sales_data.daily_revenue_model`, STRUCT(30 AS horizon, 0.8 AS confidence_level)): ### **What ML.FORECAST is:** `ML.FORECAST` is a specialized **BigQuery ML** table-valued function used to perform batch inference on trained time-series models (such as `ARIMA_PLUS` and `ARIMA_PLUS_XREG`). It projects future values across a specified temporal range while calculating lower and upper bound prediction intervals directly within SQL queries. ### **How it meets the requirements:** - **Forecasts future values (✓):** `ML.FORECAST` executes multi-step ahead time-series forecasting, generating expected values for upcoming timestamps based on learned trends and seasonality. - **Configures the forecast horizon (✓):** Setting `30 AS horizon` explicitly instructs the model to predict the next 30 discrete time intervals (days) ahead from the end of the historical training data. - **Defines the confidence level (✓):** Setting `0.8 AS confidence_level` produces prediction intervals bounded between lower and upper prediction limits at an 80% confidence interval. - **Native SQL execution (✓):** Runs directly in BigQuery without requiring data export or external machine learning pipelines. ### **Key advantages and capabilities:** - **Automated decomposition:** Automatically captures complex trend changes, daily/weekly/annual seasonality, and holiday effects. - **Prediction intervals:** Returns `forecast_value`, `standard_error`, `lower_bound`, and `upper_bound` for each step in the horizon. - **Flexible parameter tuning:** Accepts custom parameters in a `STRUCT`, including `horizon` (default is 30 or 1000 depending on cadence) and `confidence_level` (between 0.0 and 1.0; default is 0.95). - **Scalable multi-series support:** Can forecast multiple distinct time series simultaneously when trained with the `TIME_SERIES_ID_COL` parameter. ### **Why this is optimal:** `ML.FORECAST` is the standard, purpose-built function in BigQuery ML for time-series projections using `ARIMA_PLUS`. It directly implements the required horizon and confidence parameters with proper decimal formatting (`0.8` for 80%), avoiding the overhead of custom inference scripts or general-purpose prediction functions.

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Bigquery Ml Forecast Time Series Horizon Confidence

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