Accordingly, in a recent comparison of the models' forecasting accuracy, the multivariate seasonal ARIMA model (SARIMA), an expanded form of ARIMA, was shown to be the most appropriate for forecasting the number of patients admitted to the emergency department per day, as it was built to incorporate explanatory variables affecting that number [ 20].
Table 7 The statistical summary measures of model's forecast accuracy Model RMSE MAPE ARIMA (1,1,0)/EGEDCH(1,1) GED 463.93 1.650 Soft GRBFNN 407.17 1.453 GRBFNN with error-correction mechanism 295.56 1.197 SVR 392.3 1.421.
The results show that the proposed printing decision model improved forecast accuracy by 3.7%, reduced cost by 8.3%, and the contract design enhanced overall supply chain and manufacturer profitability by 0.5% and 2.7%, respectively.
Table 7 presents two statistical measures of model's forecast accuracy based on the Mean Absolute Percentage Error MAPEE) and the root mean square error (RMSE) calculated over the validation data set and shows the results of the methods used for comparison.
However, at this stage, the conclusions only show that by keeping the GARCH architecture constant, the MLP model showed significant forecast accuracy gains over the models in the first column.
Among the RBF and MLP augmented LSTAR-LST-GARCH family, the MLP-based models augmented the forecast accuracy of the LSTAR-LST-GARCH models followed by the RBF models.
Results from the study show that the Reg-SARIMA GARCH model pReg-SARIMA GARCHorecast accuracy with a modelabsolute produceserror (MAPE) of 1.42%.
This model is applied to forecast the 1-month ahead streamflow of three stations in China, and the results show that the AEEMD-ANN model can improve forecast accuracy in flood seasons, but it is not as good as ANN, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and seasonal first-order autoregressive (SAR (1)) models in dry seasons.
Elsharkawy (1998) showed that radial basis function type neural network model had better forecast accuracy than the conventional methods in terms of predicting the oil formation volume factor, solution gas oil ratio, oil viscosity, saturated oil density, under-saturated oil compressibility, and evolved gas gravity.
