Question

Which of the following is not one of the summary measures for forecast errors that is commonly used? a. RMSE (root mean square error) b. MAPE (mean absolute percentage error) c. MAE (mean absolute error) d. MFE (mean forecast error)

Answer 1

d. MFE (mean forecast error)

Explanation:

A forecast error is the difference between an observed value and its forecast. Here “error” does not mean a mistake, it means the unpredictable part of an observation.

We can measure forecast accuracy by summarizing the forecast errors in different ways.

Scale-dependent errors: The two most commonly used scale-dependent measures are based on the absolute errors or squared errors:

`\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*}`
`\text{Mean absolute error: MAE} & = \text{mean}(|e_(t)|),\\ \text{Root mean squared error: RMSE} & = \sqrt{\text{mean}(e_(t)^2)}.`

Percentage errors: The most commonly used measure is:

`\text{Mean absolute percentage error: MAPE} = \text{mean}(|p_(t)|).`

The Mean forecast error is used as a basis for following up and adjusting forecasts. Measures average deviation of forecast from actual.

Therefore, the mean forecast error (MFE) is not one of the summary measures for forecast errors that is commonly used.