Which two common forecast accuracy metrics are RMSE and MAPE?

Which two common forecast accuracy metrics are RMSE and MAPE?

• A. R-squared and adjusted R-squared
• B. AIC and BIC
• C. RMSE and MAPE
• D. VAR and ARIMA

Answer: (C)

Forecast accuracy is assessed with error metrics that quantify how far a forecast misses actual outcomes. RMSE, or root mean square error, takes the square root of the average of the squared forecast errors. Squaring the errors gives more weight to larger misses, so RMSE is especially sensitive to big forecast errors, and because it is in the same units as the target, it is easy to interpret in the context of the variable you’re predicting. MAPE, the mean absolute percentage error, averages the absolute errors expressed as a percentage of the actual values, providing a scale-free measure that lets you compare accuracy across series with different units or magnitudes. However, MAPE can be problematic if actual values are zero or near zero, since percentages can explode or become undefined. Together, RMSE and MAPE cover complementary views of forecast accuracy: RMSE emphasizes the size of errors in original units and is influenced by large errors, while MAPE gives a relative, percentage-based assessment that facilitates comparison across datasets. The other options refer to different concepts: R-squared and adjusted R-squared describe how much of the variance the model explains; AIC and BIC are information criteria used for model selection; VAR and ARIMA are types of forecasting models, not direct accuracy metrics.

Forecast accuracy is assessed with error metrics that quantify how far a forecast misses actual outcomes. RMSE, or root mean square error, takes the square root of the average of the squared forecast errors. Squaring the errors gives more weight to larger misses, so RMSE is especially sensitive to big forecast errors, and because it is in the same units as the target, it is easy to interpret in the context of the variable you’re predicting. MAPE, the mean absolute percentage error, averages the absolute errors expressed as a percentage of the actual values, providing a scale-free measure that lets you compare accuracy across series with different units or magnitudes. However, MAPE can be problematic if actual values are zero or near zero, since percentages can explode or become undefined.

Together, RMSE and MAPE cover complementary views of forecast accuracy: RMSE emphasizes the size of errors in original units and is influenced by large errors, while MAPE gives a relative, percentage-based assessment that facilitates comparison across datasets. The other options refer to different concepts: R-squared and adjusted R-squared describe how much of the variance the model explains; AIC and BIC are information criteria used for model selection; VAR and ARIMA are types of forecasting models, not direct accuracy metrics.