基于BP、LSTM和ARIMA模型的蔬菜价格预测  

Vegetable price prediction based on BP,LSTM and ARIMA models

作　　者：彭红星[1] 郑楷航 黄国彬 林督盛 阳智超 刘华鼐[2] Peng Hongxing;Zheng Kaihang;Huang Guobin;Lin Dusheng;Yang Zhichao;Liu Huanai(College of Mathematic and Information,South China Agricultural University/Key Laboratory of Key Technology for South AgriculturalMachine and Equipment,Ministry of Education,Guangzhou,510642,China;School of Chemistry and Chemical Engineering,South China Technology of University,Guangzhou,510640,China)

机构地区：[1]华南农业大学数学与信息学院/南方农业机械与装备关键技术教育部重点实验室,广州市510642 [2]华南理工大学化学与化工学院,广州市510640

出　　处：《中国农机化学报》2020年第4期193-199,共7页Journal of Chinese Agricultural Mechanization

基　　金：国家自然科学基金项目(61863011);广东省农业科技特派员项目(2018A0149);广东省农业发展和农村工作专项资金(2017SGNY001);华南农业大学2018年度大学生创新创业项目(201810564292)。

摘　　要：为系统统计蔬菜价格,实现蔬菜价格可视化并加以预测,以利于生产者科学决策。为此,首先爬取广州江南果菜批发市场所有的蔬菜价格,并对蔬菜价格的数据集进行预处理,然后建立起基于时间序列的ARIMA预测模型、BP神经网络预测模型和LSTM神经网络预测模型,通过3种模型对爬取的蔬菜价格进行分析和预测,最后将3种预测模型的实验结果进行对比。在选取的多种蔬菜的预测结果中,LSTM、BP、ARIMA模型的相对误差小于1%的平均比例分别为0.037、0.07、0.097,相对误差小于5%的平均比例分别为0.215、0.338、0.433,相对误差小于10%的平均比例分别为0.436、0.573、0.694。结果表明,ARIMA模型在预测蔬菜价格方面的准确率比LSTM、BP模型更高。The main purpose of this study is to systematically calculate vegetable prices and visualize and predict vegetable prices so as to facilitate the scientific decision-making of producers.Therefore,it was used Python Programming Language and first crawled all vegetable prices in Guangzhou Jiangnan Fruit and Vegetable Wholesale Market and pretreated the data sets of vegetable prices.Then it was established ARIMA prediction model based on time series,BP neural network prediction model and LSTM neural network prediction model.Through three models,the crawled vegetable prices were analyzed and predicted.Among the prediction results of the selected vegetables,the average proportion of the relative errors of LSTM,BP and ARIMA models less than 1%was 0.037,0.07 and 0.097respectively,the average proportion of the relative errors less than 5%was 0.215,0.338and 0.433respectively,and the average proportion of the relative errors less than 10%was 0.436,0.573and 0.694respectively.Finally,results of the comparion between three models showed that ARIMA prediction model was more accurate in predicting vegetable prices.

关 键 词：蔬菜价格 BP神经网络 LSTM神经网络 ARIMA 预测 

分 类 号：F323.7[经济管理—产业经济]