Measuring Forecast Accuracy: Approaches to Forecasting : A Tutorial

Measuring Forecast Accuracy

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

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

Period	Demand	Forecast	Error	Absolute Error
3	11	13.5	-2.5	2.5
4	9	13	-4.0	4.0
5	10	10	0	0.0
6	8	9.5	-1.5	1.5
7	14	9	5.0	5.0
8	12	11	1.0	1.0

n = 6 observations	Period	Demand	Forecast	Error	Absolute Error
	3	11	13.5	-2.5	2.5
	4	9	13	-4.0	4.0
	5	10	10	0	0.0
	6	8	9.5	-1.5	1.5
	7	14	9	5.0	5.0
	8	12	11	1.0	1.0
				-2	14

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

Rule of Thumb:

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

h2. Other Measures