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AI.EVALUATE
Evaluates the accuracy of a forecasting model by comparing predicted values
against actual historical data. It computes standard regression metrics like
MAE, MSE, and RMSE.
Syntax
SELECT
*
FROM
AI.EVALUATE(
{ TABLE `project.dataset.history_table` | (HISTORY_QUERY) },
{ TABLE `project.dataset.actual_table` | (ACTUAL_QUERY) },
data_col => 'DATA_COL',
timestamp_col => 'TIMESTAMP_COL'
[, model => 'MODEL']
[, id_cols => ID_COLS]
[, horizon => HORIZON]
)
Input Arguments
| Argument |
Requirement |
Type |
Description |
history_data |
Required |
|
The table or query containing historical data used for the forecast. |
actual_data |
Required |
|
The table or query containing the actual "ground truth" data. |
data_col |
Required |
String |
The numeric column to evaluate. |
timestamp_col |
Required |
String |
The column containing dates/timestamps. |
id_cols |
Optional |
Array<String> |
Grouping columns for multiple series. |
horizon |
Optional |
Int64 |
Number of points to evaluate. Defaults to 1024. |
model |
Optional |
String |
Model version (e.g., 'TimesFM 2.0'). |
Output Schema
| Column |
Type |
Description |
id_cols |
(As Input) |
Original identifiers for the series (if provided). |
mean_absolute_error |
FLOAT64 |
Average magnitude of errors in the predictions. |
mean_squared_error |
FLOAT64 |
Average of the squares of the errors. |
root_mean_squared_error |
FLOAT64 |
Square root of the mean squared error. |
mean_absolute_percentage_error |
FLOAT64 |
Mean absolute percentage error for the series. |
symmetric_mean_absolute_percentage_error |
FLOAT64 |
Symmetric mean absolute percentage error for the series. |
ai_evaluate_status |
STRING |
Error messages or empty string on success. |
Example: Evaluating Forecast Quality
SELECT * FROM AI.EVALUATE(
(SELECT * FROM `sales` WHERE date < '2024-01-01'),
(SELECT * FROM `sales` WHERE date >= '2024-01-01'),
data_col => 'total_sales',
timestamp_col => 'date',
id_cols => ['store_id']
);
