Stephan Kolassa and Wolfgang Schütz provide a careful look at the ratio MAD/Mean, which has been proposed as a substitute metric for the MAPE in the case of intermittent demand series. They explain how MAD/Mean can be viewed as a weighted mean of absolute percentage errors and thus as a weighted alternative to MAPE. They describe several advantages of MAD/Mean to the MAPE including applicability to inventory decisions, absence of bias in method selection, and suitability for series with intermittent as well as near-zero demands. Copyright International Institute of Forecasters, 200
Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mea...
<p>Forecasting performance (MASE) of ML and statistical methods across various horizons having appli...
The paper shows that due to the features of SKU (stock-keeping unit) demand data wellknown error mea...
The mean absolute percentage error (MAPE) is one of the most widely used measures of forecast accura...
AbstractThe mean absolute percentage error (MAPE) is one of the most widely used measures of forecas...
International audienceWe study in this paper the consequences of using the Mean Absolute Percentage ...
Forecasts of product demand are essential for short- and long-term optimization of logistics and pro...
This article displays an application of the statistical method motivated by Bruno de Finetti's opera...
Massive increases in computing power and new database architectures allow data to be stored and proc...
Abstract: This article displays an application of the statistical method moti-vated by Bruno de Fine...
Successful demand planning relies on accurate demand forecasts. Existing demand planning software ty...
<p>RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...
Includes bibliographical references (pages 56-57)Rainfall and other variables with similarly skewed ...
International audienceWe study in this paper the consequences of using the Mean Absolute Percentage ...
When comparing how well different algorithms forecast time series, researchers use an average value ...
Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mea...
<p>Forecasting performance (MASE) of ML and statistical methods across various horizons having appli...
The paper shows that due to the features of SKU (stock-keeping unit) demand data wellknown error mea...
The mean absolute percentage error (MAPE) is one of the most widely used measures of forecast accura...
AbstractThe mean absolute percentage error (MAPE) is one of the most widely used measures of forecas...
International audienceWe study in this paper the consequences of using the Mean Absolute Percentage ...
Forecasts of product demand are essential for short- and long-term optimization of logistics and pro...
This article displays an application of the statistical method motivated by Bruno de Finetti's opera...
Massive increases in computing power and new database architectures allow data to be stored and proc...
Abstract: This article displays an application of the statistical method moti-vated by Bruno de Fine...
Successful demand planning relies on accurate demand forecasts. Existing demand planning software ty...
<p>RMSE = root mean square error, MAE = mean absolute error and MAPE = mean absolute percentage erro...
Includes bibliographical references (pages 56-57)Rainfall and other variables with similarly skewed ...
International audienceWe study in this paper the consequences of using the Mean Absolute Percentage ...
When comparing how well different algorithms forecast time series, researchers use an average value ...
Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mea...
<p>Forecasting performance (MASE) of ML and statistical methods across various horizons having appli...
The paper shows that due to the features of SKU (stock-keeping unit) demand data wellknown error mea...