![]() This is the exact definition of the median. In other words, we are looking for a value that splits our dataset into two equal parts. Here is the demand per week that we observed so far:Ĭonclusion to optimize MAE (i.e., set its derivative to 0), the forecast needs to be as many times higher than the demand as it is lower than the demand. Let’s imagine a product with a low and rather flat weekly demand that has from time to time a big order (maybe due to promotions or to clients ordering in batches). ![]() One could think that using RMSE instead of MAE or MAE instead of MAPE doesn’t change anything. ![]() We went through the definition of these KPIs (bias, MAPE, MAE, RMSE), but it is still unclear what difference it can make for our model to use one instead of another. You can try this for yourself and reduce the error of one of the most accurate periods to observe the impact on MAE and RMSE.Īs you will see later, RMSE has some other very interesting properties. Clearly, RMSE emphasizes the most significant errors, whereas MAE gives the same importance to each error. Still, MAE is only reduced by 3.6% (2.33 to 2.25), so the impact on MAE is nearly twice as low. Interestingly, by just changing the error of this last period by a single unit, we decrease the total RMSE by 6.9% (2.86 to 2.66).
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