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In statistics, the mean absolute scaled error (MASE) is a measure of the accuracy of forecasts. It is the mean absolute error of the forecast values, divided by the mean absolute error of the in-sample one-step naive forecast.
17 de nov. de 2014 · Mean absolute scaled error (MASE) is a measure of forecast accuracy proposed by Koehler & Hyndman (2006). MASE = MAE MAEin−sample,naive M A S E = M A E M A E i n − s a m p l e, n a i v e. where MAE M A E is the mean absolute error produced by the actual forecast; while MAEin−sample,naive M A E i n − s a m p l e, n a i v e is ...
El error escalado absoluto medio (MASE) es una métrica de error sin escala que proporciona cada error como una proporción en comparación con el error promedio de una línea de base. Las ventajas de MASE incluyen que nunca da valores indefinidos o infinitos, por lo que es una buena opción para series de demanda intermitente (que surgen ...
11 de ene. de 2021 · In time series forecasting, Mean Absolute Scaled Error (MASE) is a measure for determining the effectiveness of forecasts generated through an algorithm by comparing the predictions with...
9 de mar. de 2017 · Calcula el error de escala absoluta de media (MASE) entre el pronóstico y los eventuales resultados. Sintaxis. MASE(X, F, M) X es el resultado eventual de la muestra de datos de una serie de tiempo (un despliegue de celdas unidimensional (Ej. fila o columna). F
Mean Absolute Scaled Error (MASE) is a scale-free error metric that gives each error as a ratio compared to a baseline’s average error. The advantages of MASE include that it never gives undefined or infinite values and so is a good choice for intermittent-demand series (which arise when there are periods of zero demand in a forecast).
Calculate the mean absolute scaled error. This metric is scale independent and symmetric. It is generally used for comparing forecast error in time series settings. Due to the time series nature of this metric, it is necessary to order observations in ascending order by time.
