The Mean Absolute Percentage Error (MAPE) is a statistical measure used to assess the accuracy of a forecasting model. It calculates the average absolute percentage error between predicted values and actual values. MAPE is particularly useful in fields like finance, supply chain management, and any domain where accurate forecasting is critical.
To compute MAPE, the following formula is used:
MAPE = (1/n) * Σ |(Actual – Forecast) / Actual| * 100%
Where:
- n is the number of observations
- Actual refers to the actual observed values
- Forecast refers to the predicted values
MAPE provides a straightforward way to express the accuracy of a model as a percentage, making it easy to interpret. A lower MAPE value indicates better predictive accuracy, while a higher value suggests poor forecasting performance. However, it is important to note that MAPE can be sensitive to small actual values, which may lead to misleading results if not handled properly.
In practice, MAPE is favored for its simplicity and ease of interpretation, but it should be used alongside other metrics for a comprehensive evaluation of model performance.