Evaluating Forecast Accuracy: MAE vs. RMSE

In the context of time-series analysis, model evaluation is a critical step in validating forecasting models. A financial analyst has developed a time-series model to forecast monthly sales figures for a retail company. To assess the model's performance, the analyst calculated the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) of the predictions.

The Mean Absolute Error (MAE) is defined as:

$$ MAE = rac{1}{n} imes extstyle igg| ext{Actual}_i - ext{Forecast}_i igg| $$

Where $ ext{Actual}_i$ is the actual value for the period, $ ext{Forecast}_i$ is the predicted value for the period, and $n$ is the total number of forecasts.

The Root Mean Squared Error (RMSE) is defined as:

$$ RMSE = igg( rac{1}{n} imes extstyle igg( ext{Actual}_i - ext{Forecast}_i igg)^2 igg)^{rac{1}{2}} $$

Which of the following statements correctly describes the relationship between MAE and RMSE when evaluating the accuracy of a forecasting model?