Multivariate time series forecast in industrial process based on XGBoost and GRU
文献类型：会议论文
| 作者 | Zhai NJ(翟乃举)1,2,4,5; Yao PF(姚培福)3; Zhou XF(周晓锋)1,4,5
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| 出版日期 | 2020 |
| 会议日期 | December 11-13, 2020 |
| 会议地点 | Chongqing, China |
| 关键词 | time series multivariate Xgboost model GRU model temperature of the heating furnace |
| 页码 | 1397-1400 |
| 英文摘要 | In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU model uses XGBoost's strong feature extraction capabilities to extract the hidden information of multiple control variables in industrial data. Next, the model uses GRU's unique gating unit to extract the timing information in the industrial data. Finally, the importance of XGBoost output variables to guide actual production and solve the problem of inexplicability of neural networks. Predicting the temperature of the heating furnace verifies that the proposed XGB-GRU is better than a single XGBoost and GRU model. And the model has a good fit to the predicted value. |
| 源文献作者 | Chengdu Global Union Academy of Science and Technology ; Chongqing Geeks Education Technology Co., Ltd ; Chongqing Global Union Academy of Science and Technology ; Chongqing Jiaotong University ; IEEE Beijing Section |
| 产权排序 | 1 |
| 会议录 | 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-5244-8 |
| 源URL | [http://ir.sia.cn/handle/173321/28322]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 通讯作者 | Zhou XF(周晓锋) |
| 作者单位 | 1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China 2.University of Chinese Academy of Sciences, Beijing, China 3.China Copper Co.LTD, Kunming, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Zhai NJ,Yao PF,Zhou XF. Multivariate time series forecast in industrial process based on XGBoost and GRU[C]. 见:. Chongqing, China. December 11-13, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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