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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (7): 18-27    DOI: 10.11925/infotech.2096-3467.2020.0323
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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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[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.

Key wordsForecasting      Machine Learning      Dressed Chicken     
Received: 16 April 2020      Published: 25 July 2020
ZTFLH:  TP393  
Corresponding Authors: Wang Jiandong     E-mail:

Cite this article:

Chen Dong,Wang Jiandong,Li Huiying,Cai Sihang,Huang Qianqian,Yi Chengqi,Cao Pan. Forecasting Poultry Turnovers with Machine Learning and Multiple Factors. Data Analysis and Knowledge Discovery, 2020, 4(7): 18-27.

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General Train of Thought
特征类别 特征序号 特征名称 特征描述
市场主体特征 F1 BREEDING_ADD_YOY 鸡鸭等家禽养殖企业和个体工商户新增数量同比值
F2 BREEDING_CANCEL_REVOKE_YOY 鸡鸭等家禽养殖企业和个体工商户注销及吊销数量同比值
F3 BREEDING_RECRUIT_YOY 鸡鸭等家禽养殖企业和个体工商户招聘岗位数量同比值
F4 FEED_ADD_YOY 鸡鸭等家禽饲料企业和个体工商户新增数量同比值
F5 FEED_CANCEL_REVOKE_YOY 鸡鸭等家禽饲料企业和个体工商户注销及吊销数量同比值
F6 FEED_RECRUIT_YOY 鸡鸭等家禽饲料企业和个体工商户招聘岗位数量同比值
F7 SLAUGHTER_ADD_YOY 鸡鸭等家禽屠宰加工企业和个体工商户新增数量同比值
F8 SLAUGHTER_CANCEL_REVOKE_YOY 鸡鸭等家禽屠宰加工企业和个体工商户注销及吊销数量同比值
F9 SLAUGHTER_RECRUIT_YOY 鸡鸭等家禽屠宰加工企业和个体工商户招聘岗位数量同比值
F10 CHICK_ADD_YOY 鸡苗种鸡企业和个体工商户新增数量同比值
F11 CHICK_CANCEL_REVOKE_YOY 鸡苗种鸡企业和个体工商户注销及吊销数量同比值
F12 CHICK_RECRUIT_YOY 鸡苗种鸡企业和个体工商户招聘岗位数量同比值
F13 MEDICINE_ADD_YOY 生产禽药企业和个体工商户新增数量同比值
F14 MEDICINE_CANCEL_REVOKE_YOY 生产禽药企业和个体工商户注销及吊销数量同比值
F15 MEDICINE_RECRUIT_YOY 生产禽药企业和个体工商户招聘岗位数量同比值
F16 BREEDING_ADD_QOQ 鸡鸭等家禽养殖企业和个体工商户新增数量环比值
F17 BREEDING_CANCEL_REVOKE_QOQ 鸡鸭等家禽养殖企业和个体工商户注销及吊销数量环比值
F18 BREEDING_RECRUIT_QOQ 鸡鸭等家禽养殖企业和个体工商户招聘岗位数量环比值
F19 FEED_ADD_QOQ 鸡鸭等家禽饲料企业和个体工商户新增数量环比值
F20 FEED_CANCEL_REVOKE_QOQ 鸡鸭等家禽饲料企业和个体工商户注销及吊销数量环比值
F21 FEED_RECRUIT_QOQ 鸡鸭等家禽饲料企业和个体工商户招聘岗位数量环比值
F22 SLAUGHTER_ADD_QOQ 鸡鸭等家禽屠宰加工企业和个体工商户新增数量环比值
F23 SLAUGHTER_CANCEL_REVOKE_QOQ 鸡鸭等家禽屠宰加工企业和个体工商户注销及吊销数量环比值
F24 SLAUGHTER_RECRUIT_QOQ 鸡鸭等家禽屠宰加工企业和个体工商户招聘岗位数量环比值
F25 CHICK_ADD_QOQ 鸡苗种鸡企业和个体工商户新增数量环比值
F26 CHICK_CANCEL_REVOKE_QOQ 鸡苗种鸡企业和个体工商户注销及吊销数量环比值
F27 CHICK_RECRUIT_QOQ 鸡苗种鸡企业和个体工商户招聘岗位数量环比值
F28 MEDICINE_ADD_QOQ 生产禽药企业和个体工商户新增数量环比值
F29 MEDICINE_CANCEL_REVOKE_QOQ 生产禽药企业和个体工商户注销及吊销数量环比值
F30 MEDICINE_RECRUIT_QOQ 生产禽药企业和个体工商户招聘岗位数量环比值
舆情信息特征 F31 CHICKEN_NUMS 网民提及鸡肉等相关舆情信息数量
F32 CHICKEN_EMOTION 网民提及鸡肉等相关舆情信息情感值
搜索意愿特征 F33 SEARCH_SPRING_FESTIVAL “过年”一词百度指数结果
F34 SEARCH_CHICKEN “鸡肉”一词百度指数结果
F35 SEARCH_CHICKEN_PRICE “鸡肉价格”一词百度指数结果
F36 SEARCH_FEED “饲料”一词百度指数结果
F37 SEARCH_BLESS “扫福”一词百度指数结果
F38 SEARCH_ONLINE_OFFICE “在线办公”一词百度指数结果
F39 SEARCH_RETURN “返乡”一词百度指数结果
F41 SEARCH_GREETINGS “拜年”一词百度指数结果
F42 SEARCH_DISEASE “疾病”一词百度指数结果
F43 SEARCH_VEGETABLES “买菜”一词百度指数结果
F44 SEARCH_EPIDEMIC “疫情”一词百度指数结果
F45 SEARCH_TICKET “抢票”一词百度指数结果
F46 SEARCH_CHICK “鸡苗”一词百度指数结果
统计数据特征 F47 PORK_NUMS 猪肉日均交易量(统计口径)
F48 EGG_NUMS 鸡蛋日均交易量(统计口径)
F49 BEEF_NUMS 牛肉日均交易量(统计口径)
F50 MUTTON_NUMS 羊肉日均交易量(统计口径)
Predict Characteristics of Dressed Chicken’s Daily Turnover (Week by Week)
Results of Random Sampling Data Set on the Stability
Comparison with Prediction Results of Different Algorithms
时间切片 训练集时间跨度 测试集时间跨度
1 第1~44周 第45周
2 第1~45周 第46周
3 第1~46周 第47周
4 第1~47周 第48周
5 第1~48周 第49周
6 第1~49周 第50周
7 第1~50周 第51周
8 第1~51周 第52周
Data Set Partition Method of Iterative Rolling Prediction Experiment
Comparison with Prediction Results of Different Algorithms
时间切片 训练集时间跨度 测试集时间跨度
1 第1~44周 第52周
2 第1~45周 第52周
3 第1~46周 第52周
4 第1~47周 第52周
5 第1~48周 第52周
6 第1~49周 第52周
7 第1~50周 第52周
8 第1~51周 第52周
Data Set Partition Method of Prediction Effect and Training Sample Number Analysis Experiment
The Relationship Between the Training Samples Needed for Prediction and the Number of Period Time
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