What is 'mean absolute scaled error' in SAP SCM-IBP-DM - Demand?

mean absolute scaled error - Overview

• Component: SCM-IBP-DM

• Component Name: Demand

• Description: A measure for forecast accuracy.

mean absolute scaled error - Details

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  Key Concepts: 
  Mean Absolute Scaled Error (MASE) is a measure of accuracy used to evaluate the performance of a forecasting model. It is calculated by taking the mean absolute error (MAE) of a forecast model and dividing it by the mean absolute error of a naïve forecast model. The naïve forecast model is a simple forecasting method that uses the most recent observation as the forecast for all future periods. 
  
  How to use it: 
  MASE is used to compare the accuracy of different forecasting models. It is calculated by taking the mean absolute error (MAE) of a forecast model and dividing it by the mean absolute error of a naïve forecast model. The lower the MASE value, the more accurate the forecast model is compared to the naïve forecast model. 
  
  Tips & Tricks: 
  When evaluating different forecasting models, it is important to consider both the MASE and MAE values. The MASE value provides an indication of how accurate a forecasting model is compared to a naïve forecast model, while the MAE value provides an indication of how accurate a forecasting model is in absolute terms. 
  
  Related Information: 
  MASE is often used in conjunction with other measures of accuracy such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). These measures provide additional information about the accuracy of a forecasting model and can be