The absolute mean error, the determination coefficient and the standard error of prediction were used as performance criteria.
For evaluating the ability of the models, root mean square errors, mean absolute errors and determination coefficient statistics were used.
The most commonly used statistical indices from previous studies include root mean square error (RMSE), determination coefficient (R 2) and mean square error (MSE) [26].
The model reliability of fit was checked by means of the determination coefficient (R).
Root mean square error, mean absolute error, mean absolute relative error and determination coefficient statistics were employed for the evaluation accuracy of each model.
Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models.
For evaluating the ability of the models, mean square error and determination coefficient criteria were used.
Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP ANN to MLP FFA models.
Root mean-squared errors (RMSE), mean-absolute errors (MAE) and determination coefficient (R2) were used for the evaluation of the models' performances.
Determination coefficient (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), SI and δ are five statistical indicators which help us to measure the precision of the designed models.
