What does a high mean absolute error indicate about a forecast model?

A high mean absolute error (MAE) signifies that the forecasted values deviate significantly from the actual values. MAE is calculated by taking the average of the absolute differences between predicted and observed values, and a higher value indicates that these differences are considerable across the dataset.

When the MAE is high, it suggests that the model is struggling to closely predict outcomes and, therefore, has a significant forecast error. This insight is crucial for practitioners, as it directs attention towards either improving the model for better accuracy or reconsidering the calculations and assumptions that were made during the forecasting process.

In contrast, a low MAE would suggest a more accurate model, whereas overfitting and underfitting relate to the model's complexity and its performance on training versus new data, not necessarily to the MAE itself. Thus, a high MAE directly reflects errors in the forecasts made by the model rather than its complexity or training effectiveness.