Mean absolute error

In statistics, the mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by
As the name suggests, the mean absolute error is an average of the absolute errors e_{i} = f_{i} − y_{i}, where f_{i} is the prediction and y_{i} the true value. Note that alternative formulations may include relative frequencies as weight factors.
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The mean absolute error is one of a number of ways of comparing forecasts with their eventual outcomes. Wellestablished alternatives are the mean absolute scaled error (MASE) and the mean squared error.^{[1]} These all summarize performance in ways that disregard the direction of over or under prediction; a measure that does place emphasis on this is the mean signed difference.
Where a prediction model is to be fitted using a selected performance measure, in the sense that the least squares approach is related to the mean squared error, the equivalent for mean absolute error is least absolute deviations.
References
Categories: Point estimation performance
 Statistical deviation and dispersion
 Statistical terminology
 Time series analysis
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