What does the mean absolute error measure assess?

The mean absolute error (MAE) measures the average of the absolute differences between predicted values and actual values. By calculating the absolute value of forecast errors, it effectively captures the magnitude of errors without considering their direction—meaning it disregards whether a forecast overestimates or underestimates the actual outcome. This characteristic makes MAE a valuable metric for evaluating the accuracy of forecasting models, as it provides a straightforward interpretation of how far off predictions are from the true results on average.

The focus on the absolute values ensures that all errors contribute positively to the overall metric, making it easier to understand and analyze the forecasting performance in a clear and quantifiable manner. This direct assessment of forecast errors is particularly important in various industries where precision in predictions is critical.