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Measuring Forecast Accuracy

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How Do We Measure Forecast Accuracy?
Measures of Forecast Accuracy
Mean Forecast Error (MFE)
Mean Absolute Deviation (MAD)
Tracking Signal
Other Measures

How Do We Measure Forecast Accuracy?

• Used to measure:
  • Forecast model bias
• Absolute size of the forecast errors
• Can be used to:
• Compare alternative forecasting models

• Identify forecast models that need adjustment (management by exception)

Measures of Forecast Accuracy

Error = Actual demand – Forecast
OR
et = At – Ft Winrar 3 90.

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Bim server archicad 22. h2. Mean Forecast Error (MFE)

For n time periods where we have actual demand and forecast values:

Ideal value = 0;
MFE > 0, model tends to under-forecast
MFE < 0, model tends to over-forecast

h2. Mean Absolute Deviation (MAD)

For n time periods where we have actual demand and forecast values:

While MFE is a measure of forecast model bias, MAD indicates the absolute size of the errors

Example

MFE = -2/6 = -0.33
MAD = 14/6 = 2.33

Conclusion: Model tends to slightly over-forecast, with an average absolute error of 2.33 units.

h2. Tracking Signal

Used to pinpoint forecasting models that need adjustment

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Rule of Thumb:

As long as the tracking signal is between –4 and 4, assume the model is working correctly

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h2. Other Measures

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CATEGORIES:
• Forecasting,