Resolved: MAPE & RMSE Example

Hi Ahmed Shaheen Mahmoud Elsayed Shaheen,

Thank you for taking the course and for your question.

Both MAPE and RMSE are used to evaluating the performance of a forecasting model. I would suggest that you look at both, as each of the two metrics provide different information about the model. Here are the differences: 

1.) MAPE (Mean Absolute Percentage Error) measures the average absolute percentage difference between the predicted values and the actual values. Here is the formula:

MAPE = (1/n) * Σ(|Actual - Predicted| / Actual) * 100

where n is the number of observations, Actual is the actual value, and Predicted is the predicted value.

MAPE is expressed in percentage terms and is used to evaluate the accuracy of a model's predictions relative to the actual values. A lower MAPE value indicates a more accurate model.

2.) RMSE (Root Mean Square Error). It measures the square root of the average of the squared differences between the predicted values and the actual values. Here is the formula:

RMSE = √[(1/n) * Σ(Actual - Predicted)^2]

where n is the number of observations, Actual is the actual value, and Predicted is the predicted value.

RMSE is expressed in the same units as the data and is used to evaluate the precision of a model's predictions relative to the actual values. A lower RMSE value indicates a more precise model.

I hope this helps.