## 4 Conclusions  
According to the characteristics of the construction machinery industry, the long-term and short-term forecasting model is proposed in this paper, which is dedicated to solving the production decision-making problem of enterprises under market behavior and also is used to build enterprise production decision support systems. First of all, a framework of a complete market decision-making system model is proposed. Then, the market forecasting method based on the combination of long-term and short-term is discussed, which includes a long-term forecasting part with a step length of 1 year and a short-term forecasting part with a step length of 3 months. Among the framework, the long-term forecasting of market capacity is studied based on the DS-SVR prediction model. Monthly data from 2006 to 2019 are collected to support the forecasting work. The forecasting method based on decomposition synthesis RBF is proposed to select affecting factors and the differential evolution algorithm to optimize the model. The long-term forecasting model is proposed to compare with the SVR model and SARIMA model. The results show that the mean error rate of the proposed decomposition-synthesis prediction method on the forecasting set is 26.61%, which has higher accuracy than SVR or SARIMA. On the other hand, the short-term forecasting method is studied. Based on the original training set data, the SARIMA model is tested with different time series lag periods. The results show that the forecasting accuracy of excavator sales decreases with the increase of lag periods. In principle, the delay time is shorter, and the effect is better. Therefore, a short-term forecasting model with a prediction step length of three periods is established. Through the practical verification, the decomposition error rates of the SARIMA model on the forecasting set are 17.92%, 20.02%, and 16.55%, and the SVR model is 44.98%, 34.24%, and 28.18%. The results show that the SARIMA model has better short-term prediction accuracy than the epsilon-SVR model. Furthermore, the improved short-term prediction algorithm is proposed based on the SARIMA and epsilon-SVR, and the differential evolution algorithm is applied to optimization. Through practical verification, the decomposition error rates of the improved short-term prediction algorithm are 13.65%, 18.83%, and 19.62%, respectively. The results show that the prediction accuracy of the SVR-SARIMA composite model is better than the SARIMA model. Moreover, we found that the short-term forecasting coefficients distribution with the improved algorithm is more inclined to the SARIMA algorithm, which showed there exists a strong autocorrelation and trend of construction machinery market. Meanwhile, multivariate regression data are also taken into account to a large \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Coefficient of SVR** & **Coefficient of SARIMA** \\ \hline Large type & 0.45253305 & 0.52512739 \\ Medium type & 0.23695408 & 0.91169819 \\ Small type & 0.34591814 & 0.71184436 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimized coefficients of the modified model. extent by the algorithm at the same time, which is considered to have a major impact on the volatility and amplitude of the construction machinery market. According to the results, the long-term and short-term forecasting methods can well reflect monthly data changes from the trend, but the accuracy of the long-term and short-term forecasts still needs to be improved in further studies. It is believed that the proposed method has great value to be applied to the construction of enterprise production decision support models. The research also has some limitations. Due to various factors affecting the construction machinery industry, only parts of upstream and downstream industrial factors are considered in this study. Some nonquantitative factors are failed to analyze effectively.