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So your forecasts are higher than the actuals…but is over-forecasting the real problem?

  

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How not to tackle bias

In previous blog posts I have discussed how to go about the process of identifying forecasting bias: systematic under- or over-forecasting.

Unfortunately, there are more ways of getting this wrong than there are of getting it right and I would like to share one that I came across recently in a major blue chip company. 

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Why size matters. Tackling forecast bias Part 2

So we learned from the last blog post that the apparently simple matter of spotting bias – systematic under or over forecasting – can get surprisingly tricky in practice if our actions are to be guided by scientific standards of evidence – which they need to be if we are actually going to improve matters.   

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Signals and noise. Tackling forecast bias: Part 1.

The average level of MAPE for your forecast is 25%.

So what?

Is it good or bad? Difficult to say.

If it is bad, what should you do? Improve…obviously. But how? 

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