基于CNN和GRU的农产品价格预测模型研究  

Research on the price forecast model of agricultural products based on CNN and GRU

作　　者：李洁琼[1] 刘振[2] LI Jie-qiong;LIU Zhen(Basic Course Teaching Department,Xi’an Vocational and Technical College,Xi’an710077,China;School of Chemical Engineering,Xi’an University of Arts and Sciences,Xi’an710065,China)

机构地区：[1]西安职业技术学院基础课教学部,西安710077 [2]西安文理学院化学工程学院,西安710065

出　　处：《湖北农业科学》2023年第1期177-181,共5页Hubei Agricultural Sciences

基　　金：陕西省教育厅2022年度一般专项科学研究计划(22JK0572);西安职业技术学院教学改革项目(2021JY03)。

摘　　要：鉴于现有预测模型无法在大数据环境下对农产品价格进行准确和快速预测,提出了一种结合卷积神经网络(CNN)和门控循环单元(GRU)的农产品价格预测模型。使用CNN获取局部特征,使用GRU获取数据的时序依赖,然后将二者获得的特征连接在一起,由解码器获得预测输出。通过试验与传统的单一模型进行比较,验证模型的优越性。结果表明,与传统预测模型相比,本研究构建的模型能有效地进行短期预测,对农产品价格预测具有一定的实际价值。In view of the fact that the existing forecast model could not accurately and rapidly predict the price of agricultural products in the large data environment,a new forecast model for the price of agricultural products based on Convolutional Neural Network(CNN)and Gated Recurrent Unit(GRU)was presented.Local features were obtained by using CNN,and time series dependence of data was attained by using GRU.Then,the features gained by the two were connected,and the predictive output was obtained through the decoder.The superiority of the model was verified by the comparative test with the traditional single model.The result showed that,compared with the traditional prediction model,the model built in this study could effectively conduct short-term prediction,and had a certain practical value for predicting the price of agricultural products.

关 键 词：农产品 价格预测模型 卷积神经网络 门控循环单元 短期预测 

分 类 号：F323[经济管理—产业经济]