## Performance evaluation
To compare the performance between forecasting methods, the solution computes the  **Mean absolute percentage error**  (MAPE). MAPE is a measure of prediction accuracy of forecasting methods, its formula is the following:  

![Screenshot 2023-01-04 at 10.06.43.png](JrrglpPlPWtP)
where  **At**  is the  **actual value**  and  **Ft**  is the  **forecast value** . Their difference is divided by the actual value At. The absolute value of this ratio is summed for every forecasted point in time and divided by the  **number of fitted points** ,  **n**.

In our case, the MAPE is calculated on the test set which is defined as the historical data's last data points and its length is similar to the horizon to forecast. The lower the MAPE, the better the forecast is at predicting the target variable. The second page of the [dashboard](article:19) shows the different results to the user:
![Screenshot 2023-01-23 at 09.44.00.png](PYuUzHmRF5s3)



