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Mean absolute scaled error : ウィキペディア英語版
Mean absolute scaled error

In statistics, the mean absolute scaled error (MASE) is a measure of the accuracy of forecasts . It was proposed in 2005 by statistician Rob J. Hyndman and Professor of Decision Sciences Anne B. Koehler, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements."〔
The mean absolute scaled error is given by
: \mathrm = \frac\sum_^n\left( \frac\sum_^n \left| Y_i-Y_\right|} \right) = \frac \left| e_t \right|}\sum_^n \left| Y_i-Y_\right|}
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 one-step "naive forecast method", which uses the actual value from the prior period as the forecast: ''F''''t'' = ''Y''''t''−1
This scale-free 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 intermittent-demand series because it never gives infinite or undefined values〔 except in the irrelevant case where all historical data are equal.〔
==See also==

* Mean squared error
* Mean absolute error
* Mean absolute percentage error

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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