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Frequently Asked Questions

How do you calculate bias?

Rick Glover on LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows: BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast).

What is bias in forecasting?

It is an average of non-absolute values of forecast errors. If it is negative, a company tends to over-forecast; if positive, it tends to under-forecast. In the machine learning context, bias is how a forecast deviates from actuals. In new product forecasting, companies tend to over-forecast.

How do you measure forecast accuracy and error?

The list of metrics to measure forecast accuracy and error is practically endless, but there are generally three main metrics to choose from. 1. Forecast Bias Forecast bias is simply the difference between forecasted demand and actual demand. Forecast Bias = S(Forecast - Actual Demand)

What is the difference between precision and bias?

The first distinction we have to make is the difference between the precision of a forecast and its bias: Bias represents the historical average error. Basically, will your forecasts be, on average, too high (i.e., you overshot the demand) or too low (i.e., you undershot the demand)? This will give you the overall direction of the error.

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