The mean absolute error is a statistical measure of how far estimates or forecasts are from actual values. It is most often used in time series, but can be applied more widely, to any sort of statistical estimate. In fact, it could be applied to any two pairs of numbers, where one set is “actual” and the other is an estimate, forecast or prediction. Alternatives include mean squared error, mean absolute deviations and median absolute deviations.
Set up the data by making two columns of data. One column should have the predicted values, or estimated values. The other should have the actual values.
Subtract the predicted value from the actual value in each row.
Take the absolute value of each row. That is, if the difference is negative, remove the negative sign. If it is positive, leave it as is.
Add up the absolute values.
Divide by n, that is, the total number of rows.