How is the mean error measure calculated?

• A. Average of absolute variances
• B. Variance between historical actual values and predicted values
• C. Sum of forecast errors
• D. Percentage of actual over predicted

Answer: (B)

The mean error measure is calculated by determining the average of the differences between historical actual values and predicted values. Essentially, this metric helps in assessing the accuracy of forecasts by quantifying how much forecasts deviate from actual outcomes. It reflects the errors in prediction by taking the variance between what was expected (or predicted) and what actually occurred.

Choosing the variance between historical actual values and predicted values captures the essence of the mean error measure since it accounts for each observed instance and allows for an aggregated view of forecast accuracy. By focusing on the discrepancies, one gains insight into whether the forecasting method systematically underestimates or overestimates actual outcomes, which is critical for improving future forecasting strategies.

The other options, while related to error measurement in some way, do not specifically define the mean error measure as precisely as this, leading to a clearer understanding of forecast performance.