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Mean absolute scaled error
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<p>In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, the mean absolute scaled error (MASE) is a measure of the <a href="/facts/Accuracy/JQ47xXDq">accuracy</a> of <a href="/facts/Forecasting/pwFtGyzM">forecasts</a>. It is the mean absolute error of the forecast values, divided by the mean absolute error of the in-sample one-step naive forecast. It was proposed in 2005 by statistician <a href="/facts/Rob_J._Hyndman/rzpx929A">Rob J. Hyndman</a> and decision scientist 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 has favorable properties when compared to other methods for calculating <a href="/facts/Forecast_error/f2Kf0mdB">forecast errors</a>, such as <a href="/facts/Root-mean-square_error/MlQVhebp">root-mean-square-deviation</a>, and is therefore recommended for determining comparative accuracy of forecasts. </p>