# Measuring Forecast Accuracy: Approaches to Forecasting : A Tutorial

Published on: Jan, 25, 2011

Professor of Operations & Supply Chain Management

## 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

## 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

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
Period Demand Forecast Error Absolute Error n = 6 observations 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

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

## 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