## 3 Results and Discussion
### Comparison of Forecast Results
#### 3.3.3 Modified Method for Short-Term Forecast  
Although the seasonal ARIMA model has achieved good results in short-term forecasting, it has limitations. The multi-source data-driven forecasting method has better application prospects. Therefore, a modified model and forecasting process is proposed for further study of the short-term forecasting effect of the decision framework aiming at overcoming the limitations. In particular, this study applies the previously drawn conclusions, including the forecasting effects and enterprise production scheduling, to set the number of forecasting periods to three periods and adopt a modified model of decomposition method to achieve decoupling between multivariate factors. The results are shown in Figure 11.  
Figure 11: _Cont_. Figure 10: A comparison of short-term prediction errors between the proposed DS-SVR model and the SARIMA model in the medium-sized type. Comparing the SARIMA, the SVR, and the modified forecasting algorithm, Figure 11 shows that the short-term market capacity prediction on large, medium, and small excavators basically conforms to the data change trend, and the SVR effect is slightly poor. Residual analysis indicates that the prediction error follows Gaussian distribution, and the average value will tend to zero under the condition of large samples. It verifies the reliability of the model. In the market capacity forecasting of large and medium excavators, the modified algorithm performs better, and the error distribution tends to zero. The qualitative analysis in Table 7 has reached the same conclusion. The RMSE and error rate of the ARIMA, SVR, and modified model on the forecasting samples decrease in turn according to the law of small to large. The MAE and MAPE also basically conform with this trend. The modified model is superior to the ARIMA model and epsilon-SVR model on the prediction ability of medium and large scale, while the ARIMA model has better performance on the small-scale type. However, simply using the SARIMA model cannot capture the nonlinear relationship in market changes in principle. On one hand, it cannot resist the influence of market risks and financial policies, which means it has certain limitations in reliability. In this case, the SARIMA model on the random offset of the error mean in Figure 11 is larger than the modified model. For further analysis, the limitation is because the SARIMA model cannot capture the nonlinear causality of market changes well; thus, this part of the information is \"leaked\" into the residual error. In contrast, the modified algorithm integrates data-driven analysis technology and can recover this part of information through multiple data regression. Therefore, it is considered to be a more scientific way to consider the long-term SVR forecasting results comprehensively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Short-Term Forecasting** & **RMSE of** & **RMSE of** & **MAE of** & **MAPE of** & **Average Error** \\  & **Model** & **Test Set** & **Forecast Set** & **Forecast Set** & **Forecast Set** & **Rate** \\ \hline \multirow{3}{*}{Large type} & SARIMA model & 387 & 315 & 292 & 18.80 & 17.92 \\  & SVR model & 407 & 791 & 521 & 25.85 & 44.98 \\  & SARIMA-SVR modified model & 229 & 240 & 99 & 8.65 & 13.65 \\ \hline \multirow{3}{*}{Medium type} & SARIMA model & 730 & 933 & 646 & 11.37 & 20.02 \\  & SVR model & 1140 & 1596 & 1159 & 23.36 & 34.24 \\  & SARIMA-SVR modified model & 494 & 878 & 420 & 10.94 & 18.83 \\ \hline \multirow{3}{*}{Small type} & SARIMA model & 1315 & 1615 & 1251 & 12.39 & 16.55 \\  & SVR model & 2157 & 2750 & 2359 & 26.78 & 28.18 \\ \cline{1-1}  & SARIMA-SVR modified model & 935 & 1915 & 1189 & 10.79 & 19.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results of the SARIMA model and SVR-SARIMA modified model. Figure 11: A comparison of short-term prediction result of modified model versus SARIMA model and the SVR model on forecast set. (**a**) indicates the curve of large-scale excavator market capacity.