Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 369-374.DOI: 10.11772/j.issn.1001-9081.2019081454

• DPCS 2019 • Previous Articles     Next Articles

Apple price prediction method based on distributed neural network

Bin LIU1,2,3, Jinrong HE4, Yuancheng LI5, Hong HAN1()   

  1. 1.College of Information Engineering,Northwest A&F University,Yangling Shaanxi 712100,China
    2.Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs (Northwest A&F University),Yangling Shaanxi 712100,China
    3.Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service (Northwest A&F University),Yangling Shaanxi 712100,China
    4.College of Mathematics and Computer Science,Yan’an University,Yan’an Shaanxi 716000,China
    5.College of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an Shaanxi 710054,China
  • Received:2019-07-31 Revised:2019-09-15 Accepted:2019-09-23 Online:2019-09-29 Published:2020-02-10
  • Contact: Hong HAN
  • About author:LIU Bin, born in 1981, Ph. D., associate professor. His research interests include parallel and distributed computing, deep learning.
    HE Jinrong, born in 1984, Ph. D., lecturer. His research interests include machine learning, computer vision.
    LI Yuancheng, born in 1981, Ph. D., lecturer. His research interests include reconfigurable architecture, high performance computing.
  • Supported by:
    the Postdoctoral Science Foundation of Shaanxi Province(2016BSHEDZZ121);the Fundamental Research Funds for the Central Universities(2452019064);the China Postdoctoral Science Foundation(2017M613216);the Key Research and Development Program of Shaanxi(2019ZDLNY07-06-01);the Natural Science Basic Research Plan in Shaanxi Province(2017JM6059);the Key Program of the National Natural Science Foundation of China(61834005);the Doctoral Starting up Foundation of Yan’an University(YDBK2019-06)

基于分布式神经网络的苹果价格预测方法

刘斌1,2,3, 何进荣4, 李远成5, 韩宏1()   

  1. 1.西北农林科技大学 信息工程学院,陕西 杨凌712100
    2.农业农村部农业物联网重点实验室(西北农林科技大学),陕西 杨凌 712100
    3.陕西省农业信息感知与智能服务重点实验室(西北农林科技大学),陕西 杨凌 712100
    4.延安大学 数学与计算机科学学院,陕西 延安 716000
    5.西安科技大学 计算机科学与技术学院,西安 710054
  • 通讯作者: 韩宏
  • 作者简介:刘斌(1981—),男,陕西渭南人,副教授,博士,CCF会员,主要研究方向:并行与分布式计算、深度学习
    何进荣(1984—),男,甘肃民勤人,讲师,博士,CCF会员,主要研究方向:机器学习、计算机视觉
    李远成(1981—),男,河南杞县人,讲师,博士,CCF会员,主要研究方向:可重构体系结构、高性能计算;
  • 基金资助:
    陕西省博士后基金资助项目(2016BSHEDZZ121);中央高校基本科研业务费专项资金资助项目(2452019064);中国博士后基金资助项目(2017M613216);陕西省重点研发计划项目(2019ZDLNY07-06-01);陕西省自然科学基础研究计划项目(2017JM6059);国家自然科学基金重点项目(61834005);延安大学博士科研启动项目(YDBK2019-06)

Abstract:

Concerning the issue that the traditional price prediction model for agricultural product cannot predict the market price of apple quickly and accurately under the big data scenario, an apple price prediction method based on distributed neural network was proposed. Firstly, the relative factors that affect the market price of apple were studied, and the historical price of apple, historical price of alternatives, household consumption level and oil price were selected as the input of the neural network. Secondly, a distributed neural network prediction model containing price fluctuation law was constructed to implement the short-term prediction for the market price of apple. Experimental results show that the proposed model has a high prediction accuracy, and the average relative error is only 0.50%, which satisfies the requirements of apple market price prediction. It indicates that the distributed neural network model can reveal the price fluctuation law and development trend of apple market price through the characteristic of self-learning. The proposed method not only can provide scientific basis for stabilizing apple market order and macroeconomic regulation of market price, but also can reduce the harms brought by price fluctuations, helping farmers to avoid the market risks.

Key words: apple price prediction, distributed neural network, Spark, short-term prediction, market price

摘要:

针对传统农产品价格预测模型在大数据场景下无法快速准确对苹果市场价格进行预测的问题,提出一种基于分布式神经网络的苹果价格预测方法。首先,研究影响苹果市场价格的相关因素,选取苹果历史价格、替代品历史价格、居民消费水平和原油价格四个特征作为神经网络模型的输入;然后,构建蕴含价格波动规律的分布式神经网络模型,实现对苹果市场价格的短期预测。实验结果显示,基于分布式神经网络的苹果市场价格短期预测模型具有较高的预测精度,平均相对误差仅为0.50%,满足苹果市场价格预测的要求。实验结果表明,分布式神经网络模型能够通过自学习特性揭示出苹果市场价格的波动规律和发展趋势,所提方法能为稳定苹果市场秩序和市场价格宏观调控提供科学依据,有助于降低价格波动带来的危害,帮助果农规避市场风险。

关键词: 苹果价格预测, 分布式神经网络, Spark, 短期预测, 市场价格

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