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| Forecasting Poultry Turnovers with Machine Learning and Multiple Factors |
Chen Dong1,Wang Jiandong1( ),Li Huiying1,Cai Sihang1,Huang Qianqian1,Yi Chengqi1,Cao Pan2,3 |
1Big Data Development Department, State Information Center, Beijing 100045, China
2Chongqing Western Institute of Big Data Advanced Application, Chongqing 401100, China
3Beijing Yidianying Technology Co., Ltd, Beijing 100073, China |
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Abstract [Objective] This paper tries to forecast the trends of poultry market influenced by multiple factors, aiming to strengthen the decision makings and policies for livestock and poultry production.[Methods] We chose 50 variables to construct machine learning models for predicting daily turnovers of dressed chicken. Our models were created based on popular machine learning algorithms.[Results] We found that GBRT, Random Forest and Elastic Net yielded stable prediction results and their MAEs were 25.30, 26.67, and 28.21 respectively. The prediction was improved with more large training sets and longer training time. We could forecast the turnovers of three periods in advance.[Limitations] The training sets needs to include more features and historical data.[Conclusions] The proposed models could quantatively assess and forecast the impacts of emergencies on industrial output, which imrpoves governmental policy making.
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Received: 16 April 2020
Published: 25 July 2020
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Corresponding Authors:
Wang Jiandong
E-mail: wangjd@sic.gov.cn
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