Respuesta :
Answer:
d. The mean absolute percentage error (MAPE) does not depend on the units of the forecast variable.
Step-by-step explanation:
A forecast error is the difference between the actual or real and the predicted or forecast value of a time series or any other phenomenon of interest. Here “error” does not mean a mistake, it means the unpredictable part of an observation.
There are many different ways to summarize forecast errors in order to provide meaningful information.
Scale-dependent errors. The forecast errors are on the same scale as the data. The two most commonly used scale-dependent measures are based on the absolute errors or squared errors:
[tex]\begin{align*} \text{Mean absolute error: MAE} & = \text{mean}(|e_{t}|),\\ \text{Root mean squared error: RMSE} & = \sqrt{\text{mean}(e_{t}^2)}.\end{align*}[/tex][tex]\text{Mean absolute error: MAE} & = \text{mean}(|e_{t}|),\\ \\\text{Root mean squared error: RMSE} & = \sqrt{\text{mean}(e_{t}^2)}.[/tex]
Percentage errors. Percentage errors have the advantage of being unit-free, and so are frequently used to compare forecast performances between data sets. The most commonly used measure is:
[tex]\text{Mean absolute percentage error: MAPE} = \text{mean}(|p_{t}|).[/tex]
