Mean absolute scaled error

In statistics, the mean absolute scaled error (MASE) is a measure of the accuracy of forecasts . It was proposed in 2006 by Australian statistician Rob Hyndman, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements."^{[1]}
The mean absolute scaled error is given by
 ^{[2]}
where the numerator e_{t} is the forecast error for a given period, defined as the actual value (Y_{t}) minus the forecast value (F_{t}) for that period: e_{t} = Y_{t} − F_{t}, and the denominator is the average forecast error of the onestep "naive forecast method", which uses the actual value from the prior period as the forecast: F_{t} = Y_{t−1}^{[3]}
This scalefree error metric "can be used to compare forecast methods on a single series and also to compare forecast accuracy between series. This metric is well suited to intermittentdemand series^{[clarification needed]} because it never gives infinite or undefined values^{[1]} except in the irrelevant case where all historical data are equal.^{[2]}
See also
References
 ^ ^{a} ^{b} Hyndman, R. J. (2006). "Another look at measures of forecast accuracy", FORESIGHT Issue 4 June 2006, pg46 [1]
 ^ ^{a} ^{b} Hyndman, R. J. and Koehler A. B. (2006). "Another look at measures of forecast accuracy." International Journal of Forecasting volume 22 issue 4, pages 679688. doi:http://dx.doi.org/10.1016/j.ijforecast.2006.03.001
 ^ Hyndman, Rob et al, Forecasting with Exponential Smoothing: The State Space Approach, Berlin: SpringerVerlag, 2008. ISBN 9783540719168.
Categories: Point estimation performance
 Statistical deviation and dispersion
 Statistical terminology
 Time series analysis
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