*2.7. Evaluation Index*

In this study, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE) were selected as the basis for judging the prediction effect of the model. The calculation formulae are as follows:

$$\text{MSE} = \frac{1}{N} \sum\_{t=1}^{N} \left( y\_t - \overline{y\_t} \right)^2 \tag{12}$$

$$\text{RMSE} = \sqrt{\frac{1}{N} \sum\_{t=1}^{N} \left( y\_t - \overline{y\_t} \right)^2} \tag{13}$$

$$\text{MAE} = \frac{1}{N} \sum\_{t=1}^{N} |(y\_t - \overline{y\_t})| \tag{14}$$

$$\text{MAPE} = \frac{1}{N} \sum\_{t=1}^{N} \left| \frac{y\_t - \overline{y\_t}}{y\_t} \right| \tag{15}$$

where *N* represents the total data volume, *yt* represents the real value, and *yt* represents the predicted value.

MAE is used to measure the mean absolute error between the predicted and actual values, RMSE is used to measure the deviation between the predicted and actual values (which is sensitive to outliers), and MAPE is used to measure the average relative error between the predicted and actual values.
