Table 2.

Three metric rules for point forecasting.

Metric	Definition	Equation
MAE	Mean absolute error	$\mathrm{MAE}=\frac{1}{n}{\displaystyle \sum _{i=1}^n\left|y_i-y_i^{\prime }\right|}$
MAPE	Mean absolute percentage error	$\mathrm{MAPE}=\frac{1}{n}{\displaystyle \sum _{i=1}^n\left|\frac{y_i-y_i^{\prime }}{y_i}\right|}\times 100\%$
RMSE	Root mean square error	$\mathrm{RMSE}=\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^n{\left(\frac{y_i-y_i^{\prime }}{y_i}\right)}^2}}$
R2	Goodness of fit	$R^2=1-\frac{{\displaystyle {\sum }_{i=1}^n{(y_i-y_i^{\prime })}^2}}{{\displaystyle {\sum }_{i=1}^n{(y_i-\overline{y})}^2}}$

y_i and $y_i^{\prime }$ denote the actual values and forecasting values, respectively. represents the average of actual values. The R² was also utilized to evaluate the fitness performance in the process of distribution fitting, where y_i, $y_i^{\prime }$ and represent the observed cumulative probability, estimated cumulative probability and the average of the observed cumulative probability, respectively.