Forecasting Accuracy
The forecast error is the difference between the actual value and the forecast value for the corresponding period.
where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.
Measures of aggregate error:
| Mean absolute error (MAE) | |
| Mean Absolute Percentage Error (MAPE) | |
| Mean Absolute Deviation (MAD) | |
| Percent Mean Absolute Deviation (PMAD) | |
| Mean squared error (MSE) | |
| Root Mean squared error (RMSE) | |
| Forecast skill (SS) | |
| Average of Errors (E) |
Business forecasters and practitioners sometimes use different terminology in the industry. They refer to the PMAD as the MAPE, although they compute this as a volume weighted MAPE. For more information see Calculating demand forecast accuracy.
Reference class forecasting was developed to increase forecasting accuracy by framing the forecasting problem so as to take into account available distributional information. Daniel Kahneman, winner of the Nobel Prize in economics, calls the use of reference class forecasting "the single most important piece of advice regarding how to increase accuracy in forecasting.” Forecasting accuracy, in contrary to belief, cannot be increased by the addition of experts in the subject area relevant to the phenomenon to be forecast.
See also
- Calculating demand forecast accuracy
- Consensus forecasts
- Forecast error
- Predictability
- Prediction intervals, similar to confidence intervals
- Reference class forecasting
Read more about this topic: Forecasting
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