MAPE (Mean Absolute Percentage Error)

Information

• MAPE (Mean Absolute Percentage Error) is a measure of the accuracy of a forecasting model, expressed as a percentage of the actual values.
• MAPE is defined as the average of the absolute percentage errors (APEs) over the forecast horizon, multiplied by 100% to express it as a percentage:
  • MAPE = (1/n) * sum(|(actual - forecast)/actual|) * 100%
• APE measures the size of the errors in the model, relative to the actual values, and thus reflects the magnitude of the error.
• MAPE reflects the average size of the errors in the model, relative to the actual values, and thus provides a standardized measure of the model's performance.
• MAPE is a useful metric when the target variable has a non-zero mean and a non-zero range of values, as it provides a meaningful interpretation of the errors in percentage terms.
• MAPE can be used to compare the performance of different forecasting models or to track the performance of the same model over time.
• A lower MAPE indicates a more accurate and reliable model, while a higher MAPE indicates a less accurate and more variable model.
• MAPE has some limitations, such as being sensitive to extreme values and not being suitable for forecasting models with zero or near-zero actual values.
• MAPE can be used in combination with other metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), to provide a more comprehensive evaluation of the model's performance.