# MAE (Mean Absolute Error)

The simplest measure of forecast accuracy is called Mean Absolute Error (MAE). Mean Absolute Error is simply, as the name suggests, the mean of the absolute errors. The absolute error is the absolute value of the difference between the forecasted value and the actual value. Mean Absolute Error measures accuracy for continuous variables. Mean Absolute Error tells us how big of an error we can expect from the forecast on average. The Mean Absolute Error measures the average magnitude of the errors in a set of predictions, without considering their direction. The Mean Absolute Error is the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight.
Both Mean Absolute Error and Root Mean Square Error express average model prediction error in units of the variable of interest. The Mean Absolute Error and the Root Mean Square Error can range from 0 to ∞ and are indifferent to the direction of errors.

|Oi - Pi| = the absolute errors and Σ = summation symbol

Paste 2-columns data here (obs vs. sim). In format of excel, text, etc. Separate it with space: