Dou, Zixin and Sun, Yanming and Zhang, Yuan and Wang, Tao and Wu, Chuliang and Fan, Shiqi (2021) Regional manufacturing industry demand forecasting: A deep learning approach. Applied Sciences, 11 (13). ISSN 2076-3417, DOI https://doi.org/10.3390/app11136199.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) [71571072], National Social Science Foundation [18BGL236], Guangdong Province Key Research and Development Project [2020B0101050001], Special Fund for Science and Technology Innovation Strategy of Guangdong Province [pdjh2021b0405] |
| Uncontrolled Keywords: | Manufacturing industry; Demand forecasting; Influence factor; Deep learning |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of the Built Environment |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 15 Aug 2022 01:44 |
| Last Modified: | 15 Aug 2022 01:44 |
| URI: | http://eprints.um.edu.my/id/eprint/28498 |
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