Which measure assesses the degree to which forecasted values deviate from actual values?

The measure that assesses the degree to which forecasted values deviate from actual values is widely recognized as the root mean square error (RMSE). This statistical metric provides a way to quantify the accuracy of a model by calculating the square root of the average of the squares of the errors—in other words, the differences between predicted and actual values. By squaring the errors, RMSE emphasizes larger deviations and therefore is particularly sensitive to outliers. This sensitivity makes it a valuable tool when it is critical to minimize large discrepancies between predicted and actual outcomes.

Other measures may also assess forecast accuracy, but they do so differently. For instance, mean absolute error focuses on the average magnitude of errors without regard to their direction, while mean squared error emphasizes larger deviations by squaring the errors. Mean absolute percentage error provides a percentage-based assessment, aiding comparison across different scales. However, RMSE provides a comprehensive, sensitive measure that accurately captures the overall forecast performance in relation to actual values, which is beneficial when making predictions in various business scenarios.