Which metrics are commonly used to assess forecast accuracy?

• A. MAE and MAPE
• B. R-squared and p-values
• C. CV and standard deviation
• D. Profit margin and ROA

Answer: (A)

Forecast accuracy is about how close predictions are to actual outcomes. Two widely used measures are mean absolute error and mean absolute percentage error. MAE averages the absolute differences between forecasted and actual values, providing a straightforward average error in the same units as the data. MAPE does the same in percentage terms, which makes it easy to compare forecast performance across series with different scales. These metrics directly reflect the size of forecast errors, which is what accuracy seeks to quantify. In contrast, R-squared and p-values come from regression analysis and relate to how well the model explains variance or whether a relationship is statistically significant, not the magnitude of forecast errors. Coefficient of variation and standard deviation describe data dispersion, not forecast error magnitude. Profit margin and ROA are financial performance indicators, not measures of forecast accuracy. Therefore, MAE and MAPE are the commonly used metrics for assessing forecast accuracy.