In forecasting models, which criterion measures how closely predictions match actual results?

In forecasting models, which criterion measures how closely predictions match actual results?

• A. Accuracy of prediction
• B. Cost
• C. Speed
• D. Flexibility

Answer: (A)

Accuracy of prediction measures how closely forecasts match actual results. In forecasting, you evaluate accuracy using error metrics such as mean absolute error, root mean squared error, or mean absolute percentage error—the smaller these errors, the closer the predictions are to what actually occurred. This directly captures how well the model’s outputs align with reality, which is why it’s the best criterion for gauging forecast quality. Other attributes describe different aspects: cost concerns the price of running the model, speed refers to how quickly forecasts are produced, and flexibility indicates how well the model adapts to new conditions. While useful, they don’t quantify how accurately the forecasts reflect actual outcomes.

Accuracy of prediction measures how closely forecasts match actual results. In forecasting, you evaluate accuracy using error metrics such as mean absolute error, root mean squared error, or mean absolute percentage error—the smaller these errors, the closer the predictions are to what actually occurred. This directly captures how well the model’s outputs align with reality, which is why it’s the best criterion for gauging forecast quality. Other attributes describe different aspects: cost concerns the price of running the model, speed refers to how quickly forecasts are produced, and flexibility indicates how well the model adapts to new conditions. While useful, they don’t quantify how accurately the forecasts reflect actual outcomes.