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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 : 〔 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）』 ■ウィキペディアで「mean absolute scaled error」の詳細全文を読む スポンサード リンク
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