# Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach
###### Abstract  
With the rapid development of the manufacturing industry, demand forecasting has been important. In view of this, considering the influence of environmental complexity and diversity, this study aims to find a more accurate method to forecast manufacturing industry demand. On this basis, this paper utilizes a deep learning model for training and makes a comparative study through other models. The results show that: (1) the performance of deep learning is better than other methods; by comparing the results, the reliability of this study is verified. (2) Although the prediction based on the historical data of manufacturing demand alone is successful, the accuracy of the prediction results is significantly lower than when taking into account multiple factors. According to these results, we put forward the development strategy of the manufacturing industry in Guangdong. This will help promote the sustainable development of the manufacturing industry.  
manufacturing industry; demand forecasting; influence factor; deep learning +
Footnote †: journal:  
(SVM) [11], and neural network (NN) [12]. Particularly, SVM and neural networks are the most prominent. In terms of SVM, [PERSON] et al. [13] used SVM to predict municipal solid waste and found that it was more robust. [PERSON] et al. [14] used SVM to estimate the energy consumption of hotel buildings and found that the error of the results was small. In terms of NN, [PERSON] et al. [15] used a linear regression model to predict the demand for private housing. [PERSON]'s [16] research found that although the traditional prediction methods can achieve good prediction results in the field of tourism demand, the NN prediction results are better. [PERSON] et al. [17] found that the artificial neural network method is better than other methods. [PERSON] et al. [18] used NN to predict restaurant demand. However, the NN is better than traditional methods, but in terms of the correlation between predicted data and actual data, traditional methods are better than NN. This shows that NN is not sufficient to deal with the relationship between data in some aspects. In addition, few scholars apply deep learning to industrial demand forecasting.  
To address these knowledge gaps: (1) considering the environmental complexity and diversity of the manufacturing, this paper constructs the MIDF system to improve the accuracy of MIDF. (2) In this context, the deep learning model is established to forecast the manufacturing industry demand with the minimum error rate. Therefore, this study enables us to reveal the influence of variables and how the deep learning model works on these variables.  
The rest of this paper is as follows (as shown in Figure 1): Section 2 summarizes the literature reviews. In Section 3, we introduce the research framework, namely, the prediction system, research model, and evaluation methods. The next section is an experiment, including alternative models and demand forecasting. Section 5 discusses theoretical and practical implications. Finally, the conclusion is presented.  
Figure 1: The graphical framework.