## 1 Introduction
Considering the high-level noise of the time series data, [PERSON] et al. designed a fuzzy group method of data handling neural network method to predict the power generation of wind turbines [12]. Moreover, the uncertainties of parameters and over-fitting problems of the neural network model caused by insufficient data volume would directly decrease forecasting performance [13; 14]. In this case, the support vector machine model (SVM) was applied in the forecasting model [15; 16]. [PERSON] and [PERSON] studied the influence of kernel functions and penalty factors on SVM forecasting models [17; 18]. Moreover, the nature of high dimensional candidate features was analyzed to improve the sensitivity and stability of the SVM forecasting model [19]. In addition, to overcome defects of the characteristic confusion phenomenon, the key characteristics were isolated into different subsystems to enhance performance through the combination of different methods. [PERSON] used the gray model (GM(1,1)) to predict energy consumption in Shandong Province and compounded autoregressive integrated moving average method (ARIMA) to correct the residual error [20]. In [PERSON] et al. 's study, they applied ARIMA and the exponential smoothing composite method to predict long-term power consumption in various countries [21]. [PERSON] et al. proposed a combination method of SVR and ensemble learning [22]. [PERSON] et al. applied a combined model using the least squares regression, multiple adaptive regression, and RBF multilayer perceptron to build a forecasting model by ranking [23]. [PERSON] et al. proposed a combined algorithm that is based on the chaos theory and the SVM to analyze the characteristics of time series in phase space and predict traffic flow [24]. [PERSON] et al. proposed an improved ICA neural network model to analyze independent components [25]. [PERSON] et al. devised an integratedmodel based on Wavelet Transform (WT) and Artificial Neural Network (ANN) for day-ahead prediction of electric power consumption in microgrids [26]. [PERSON] et al. designed a method based on long short-term memory(LSTM) model optimized by multi-verse optimization(MVO) algorithm to predict manufacturing pollution emissions and applied the mutual information method to analyze related factors [27]. [PERSON] et al. proposed an adaptive network-based fuzzy inference system (ANFIS) to forecast the demand of automobile sales [28]. However, the factors that act on the construction machinery market are complex. There is a research gap in developing a suitable demand forecasting method that can reflect the characteristics of the construction machinery industry. On the other hand, most of the proposed prediction research was conducted separately in the short-term and long-term. The actual decision-making process, which develops a combination of both long-term and short-term features, will make a better interpretation of the market forecasting, which is of great significance to the construction machinery industry. This paper proposed a composite decision-making model using both the long-term (based on the annual data) and the short-term (based on the monthly data) to analyze the market demand of excavators. The results proved superior to other methods and were applied to production guidance. The main innovations and contributions of this paper are as follows:  
In order to explore the internal relationship between data, we proposing to use the recursive feature elimination method to reduce the dimensionality of high-dimensional features by comparing filtering-based methods and packaging methods and using it to define and rank the relevant factors that affect the demand for different types of construction machinery. 1. The dimension reduction was carried out based on the combination of interval decoupling and recursive feature elimination. Additionally, define and rank the relevant factors that affect the demand for different types of construction machinery. 2.