Table 3From “Forecast accuracy can hide offsetting errors”
One word called accuracy hides several measures
Table 3One word called accuracy hides several measures
| Object | What it answers | What it can hide |
|---|---|---|
| Mean signed error | Is the selected set directionally high or low? | Opposite errors and the size of each miss |
| Mean absolute error | How large is the typical miss in the selected unit? | Whether errors are high or low |
| Root mean squared error | How strongly do large misses affect the summary? | The reason for an extreme miss and the ordinary case |
| Median absolute error | What is the middle miss after magnitude is taken? | A small number of severe tail errors |
| Quantiles or tail shares | How is error distributed across cases? | The causal reason for the distribution |
| Error by horizon or segment | Where does the process behave differently? | The aggregate average across unlike objects |
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Revenue operations & AI
Forecast accuracy can hide offsetting errors
A forecast can be unbiased on average while missing every period: keep signed error, absolute error, distribution, horizon, and decision cost separate.
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