Mean Absolute Error
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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 :\mathrm{MAE} = \frac{1}{n}\sum_{i=1}^n \left| f_i-y_i\right| =\frac{1}{n}\sum_{i=1}^n \left| e_i \right|. 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.The mean absolute error is a common measure of forecast error in time series analysis, where the terms mean absolute deviation is sometimes used in confusion with the more standard definition of mean absolute deviation. The same confusion exists more generally.
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