MAE (Mean Absolute Error)
The Mean Absolute Error (MAE) serves as a fundamental metric for assessing forecast accuracy, particularly in the context of continuous variables. It represents the average magnitude of errors between predicted and actual values, offering insights into the typical deviation of forecasts from observed data.
To calculate MAE, we first determine the absolute difference between each forecasted value and its corresponding actual value. These absolute errors are then averaged across all predictions, providing a measure of central tendency for the errors. Unlike some other metrics, MAE does not consider the direction of errors, treating both overestimations and underestimations equally.
MAE offers a straightforward interpretation: a lower MAE indicates better model performance, as it signifies smaller deviations between forecasts and actual outcomes. However, it's important to note that MAE does not provide information about the variability or dispersion of errors, focusing solely on their average magnitude.
In comparison to Root Mean Square Error (RMSE), another commonly used metric, MAE offers a simpler interpretation and calculation. While RMSE places greater emphasis on larger errors due to its squaring of individual errors, MAE maintains a linear relationship with error magnitude. Consequently, MAE may be preferred in situations where extreme errors should not be disproportionately penalized.
Both MAE and RMSE range from 0 to ∞, with lower values indicating better model performance. They are agnostic to the direction of errors, making them suitable for evaluating forecast accuracy across a wide range of applications.
Σ|Oi - Pi| represents the sum of absolute errors, where |Oi - Pi| denotes the absolute difference between each observed value (Oi) and its corresponding predicted value (Pi). The symbol Σ signifies summation, indicating that we are summing up these absolute differences across all data points in the dataset.
How To Cite
Please provide the data in a two-column format (observed vs. simulated). You can copy from Excel, text, or any other format, separated by space.