MAE
Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction. It is commonly used to evaluate the accuracy of forecasting models, particularly in contexts where the scale of errors matters more than their direction.
How MAE Works
MAE provides a straightforward way to assess forecast accuracy.
- Calculate absolute errors: Subtract each predicted value from the actual value and take the absolute value of each difference.
- Sum absolute errors: Add up all the absolute errors obtained in the previous step.
- Average the sum: Divide the total sum by the number of observations to get the MAE.
Strengths and Limitations
MAE is informative when the magnitude of errors is more critical than their direction, offering a clear average error size. However, it can be misleading if large errors are rare but impactful, as it does not penalize large deviations as severely as Root Mean Squared Error (RMSE) does. RMSE can be a complementary metric in such cases.
MAE in Commodity Forecasting
In commodity markets, MAE is used to evaluate the accuracy of price forecasts for assets like oil and natural gas. It helps analysts understand the typical prediction error size, aiding in model selection and refinement for better market predictions.
