Apple price prediction method based on distributed neural network

doi:10.11772/j.issn.1001-9081.2019081454

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.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.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

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

CLC Number:

TP319

Bin LIU, Jinrong HE, Yuancheng LI, Hong HAN. Apple price prediction method based on distributed neural network[J]. Journal of Computer Applications, 2020, 40(2): 369-374.

刘斌, 何进荣, 李远成, 韩宏. 基于分布式神经网络的苹果价格预测方法[J]. 《计算机应用》唯一官方网站, 2020, 40(2): 369-374.

Figures/Tables 6

References 15

1	刘媛媛，王晖，郭躬德，等. 基于多角度多区域特征融合的苹果分类方法［J］. 计算机应用， 2018， 38（5）： 1309-1314. 10.11772/j.issn.1001-9081.2017102412
	LIU Y Y， WANG H， GUO G D， et al. Multi-perspective multi-region feature fusion for apple classification［J］. Journal of Computer Applications， 2018， 38（5）： 1309-1314. 10.11772/j.issn.1001-9081.2017102412
2	刘峰，王儒敬，李传席. ARIMA模型在农产品价格预测中的应用［J］.计算机工程与应用， 2009， 45（25）：238-239， 248. 10.3778/j.issn.1002-8331.2009.25.073
	LIU F， WANG R J， LI C X. Application of ARIMA model in agricultural product price forecasting［J］. Computer Engineering and Applications， 2009， 45（25）： 238-239， 248. 10.3778/j.issn.1002-8331.2009.25.073
3	王川，赵俊晔，赵友森. 组合预测模型在农产品价格短期预测中的应用——以苹果为例的实证分析［J］. 系统科学与数学， 2013， 33（1）：89-96.
	WANG C， ZHAO J Y， ZHAO Y S. The application of combination model on short-term price forecasting of agriculture products — the empirical analysis of apple price［J］. Journal of System Science and Mathematical Sciences， 2013， 33（1）： 89-96.
4	丁慧娟，张金磊，陈建中，等.ARIMA模型和灰色模型在农产品价格预测中的应用比较［J］.安徽农业科学，2018，24（46）：191-194. 10.3969/j.issn.0517-6611.2018.24.061
	DING H J， ZHANG J L， CHEN J Z， et al. Comparison of ARIMA model and grey model in agricultural product price forecasting［J］. Journal of Anhui Agricultural Sciences， 2018， 24（46）：191-194. 10.3969/j.issn.0517-6611.2018.24.061
5	钱晔，孙吉红，彭琳，等.基于云计算的特色农庄平台设计与探讨［J］.计算机技术与发展，2017，27（2）：174-177. 10.3969/j.issn.1673-629X.2017.02.040
	QIAN Y， SUN J H， PENG L， et al. Design and discussion of characteristic farm platform based on cloud computing［J］. Computer Technology and Development， 2017， 27（2）： 174-177. 10.3969/j.issn.1673-629X.2017.02.040
6	姚冠新，顾晴.基于经验模态分解和支持向量机的农产品价格短期预测［J］.江苏农业科学，2014，42（9）：402-405. 10.3969/j.issn.1002-1302.2014.09.135
	YAO G X， GU Q. Short-term forecast of agricultural product price based on empirical mode decomposition and support vector machine［J］. Jiangsu Agricultural Sciences， 2014， 42（9）： 402-405. 10.3969/j.issn.1002-1302.2014.09.135
7	张永礼，赵蕾，董志良.基于信息粒化和PSO-SVR模型的棉花价格波动区间和变化趋势预测［J］. 广东农业科学， 2015， 42（11）：180-185. 10.3969/j.issn.1004-874X.2015.11.032
	ZHANG Y L， ZHAO L， DONG Z L. Prediction of fluctuation range and change trend of cotton price based on fuzzy information granulation and PSO-SVR model［J］. Guangdong Agricultural Sciences， 2015， 42（11）：180-185. 10.3969/j.issn.1004-874X.2015.11.032
8	ZHANG W， CHEN H， WANG M. A forecast model of agricultural and livestock products price［J］. Applied Mechanics and Materials， 2010， 20/21/22/23： 1109-1114. 10.4028/www.scientific.net/amm.20-23.1109
9	许杞刚，刘明军，李海. 基于改进KNN算法的农产品价格预测模型［J］. 济南大学学报（自然科学版）， 2014， 28（2）：114-117. 10.13349/j.cnki.jdxbn.2014.02.007
	XU Q G， LIU M J， LI H. Agricultural product price forecasting model based on improved KNN algorithm［J］. Journal of University of Jinan （Science and Technology）， 2014， 28（2）： 114-117. 10.13349/j.cnki.jdxbn.2014.02.007
10	苏照军，郭锐锋，高岑，等. 基于组合模型的农产品物价预测算法［J］. 计算机系统应用， 2019， 28（5）： 185-189.
	SU Z J， GUO R F， GAO C， et al. Agricultural product price forecasting algorithm based on combination model［J］. Computer Systems and Applications， 2019， 28（5）：185-189.
11	段青玲，张磊，魏芳芳，等.基于时间序列GA-SVR的水产品价格预测模型及验证［J］.农业工程学报，2017，33（1）：308-314. 10.11975/j.issn.1002-6819.2017.01.042
	DUAN Q L， ZHANG L， WEI F F， et al. Forecasting model and validation for aquatic product price based on time series GA-SVR［J］. Transactions of the Chinese Society of Agricultural Engineering， 2017， 33（1）： 308-314. 10.11975/j.issn.1002-6819.2017.01.042
12	罗长寿.基于神经网络与遗传算法的蔬菜市场价格预测方法研究［J］.科技通报，2011，27（6）：881-885，894. 10.3969/j.issn.1001-7119.2011.06.015
	LUO C S. Research on the prediction method of vegetable price based on neural network and genetic algorithm［J］. Bulletin of Science and Technology， 2011， 27（6）： 881-885， 894. 10.3969/j.issn.1001-7119.2011.06.015
13	王向斌.陕西省苹果市场影响因素分析［D］．杨凌：西北农林科技大学， 2015：13-19. 10.33582/2637-7721/1005
	WANG X B. Analysis of influencing factors of apple market in Shaanxi province［D］. Yangling： Northwest A&F University， 2015：13-19. 10.33582/2637-7721/1005
14	冯多，林政，付鹏，等.基于卷积神经网络的中文微博情感分类［J］. 计算机应用与软件， 2017， 34（4）：157-164， 177. 10.3969/j.issn.1000-386x.2017.04.027
	FENG D， LIN Z， FU P， et al. Chinese micro-blog emotion classification based on CNN［J］. Computer Applications and Software， 2017， 34（4）： 157-164， 177. 10.3969/j.issn.1000-386x.2017.04.027
15	张国云，向灿群，吴健辉，等.基于改进BP网络的车牌字符识别方法研究［J］.计算机应用与软件，2017，34（4）：243-248. 10.3969/j.issn.1000-386x.2017.04.041
	ZHANG G Y， XIANG C Q， WU J H， et al. Research on license plate character recognition method based on improved BP network［J］. Computer Applications and Software， 2017， 34（4）： 243-248. 10.3969/j.issn.1000-386x.2017.04.041

配置项	参数值
CPU型号	Intel Xeon E5-2650 @2.30 GHz
处理器数	20
内存	32 GB
硬盘	1 TB
操作系统	CentOS 6.6
Spark	Spark-1.3.1
JDK	JDK1.7-x64
Scala	Scala 2.10.4
MySQL	MySQL Server-5.7.11
Hadoop	Hadoop 2.6
节点数	4

配置项	参数值
CPU型号	Intel Xeon E5-2650 @2.30 GHz
处理器数	20
内存	32 GB
硬盘	1 TB
操作系统	CentOS 6.6
Spark	Spark-1.3.1
JDK	JDK1.7-x64
Scala	Scala 2.10.4
MySQL	MySQL Server-5.7.11
Hadoop	Hadoop 2.6
节点数	4

预测日期	白水地区				洛川地区
预测日期	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%
2016-08-06	4.10	4.12	0.020 0	0.49	4.10	4.16	0.060 0	1.46
2016-08-07	4.10	4.12	0.020 0	0.49	4.05	4.10	0.050 0	1.23
2016-08-08	4.15	4.14	-0.010 0	0.24	4.10	4.13	0.030 0	0.73
2016-08-09	4.10	4.11	0.010 0	0.24	4.10	4.10	0.000 0	0.00
2016-08-10	4.10	4.10	0.000 0	0.00	4.10	4.11	0.010 0	0.24
2016-08-11	4.15	4.10	-0.050 0	0.12	4.05	4.08	0.030 0	0.74
2016-08-12	4.20	4.16	-0.040 0	0.95	4.05	4.06	0.010 0	0.25
平均值	4.13	4.12	-0.004 2	0.36	4.08	4.11	0.027 1	0.66
预测日期	千阳地区				全国
预测日期	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%
2016-08-06	5.10	5.05	-0.050 0	0.98	3.06	3.08	0.020 0	0.65
2016-08-07	5.10	5.06	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-08	5.12	5.08	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-09	5.14	5.10	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-10	5.14	5.12	-0.020 0	0.39	3.08	3.08	0.000 0	0.00
2016-08-11	5.13	5.12	-0.010 0	0.19	3.07	3.08	0.010 0	0.32
2016-08-12	5.13	5.10	-0.030 0	0.58	3.08	3.08	0.000 0	0.00
平均值	5.12	5.09	0.032 9	0.64	3.08	3.08	0.008 6	0.28

预测日期	白水地区				洛川地区
预测日期	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%
2016-08-06	4.10	4.12	0.020 0	0.49	4.10	4.16	0.060 0	1.46
2016-08-07	4.10	4.12	0.020 0	0.49	4.05	4.10	0.050 0	1.23
2016-08-08	4.15	4.14	-0.010 0	0.24	4.10	4.13	0.030 0	0.73
2016-08-09	4.10	4.11	0.010 0	0.24	4.10	4.10	0.000 0	0.00
2016-08-10	4.10	4.10	0.000 0	0.00	4.10	4.11	0.010 0	0.24
2016-08-11	4.15	4.10	-0.050 0	0.12	4.05	4.08	0.030 0	0.74
2016-08-12	4.20	4.16	-0.040 0	0.95	4.05	4.06	0.010 0	0.25
平均值	4.13	4.12	-0.004 2	0.36	4.08	4.11	0.027 1	0.66
预测日期	千阳地区				全国
预测日期	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%	真实值/（元·斤^-1）	预测值/（元·斤^-1）	绝对误差/（元·斤^-1）	相对误差/%
2016-08-06	5.10	5.05	-0.050 0	0.98	3.06	3.08	0.020 0	0.65
2016-08-07	5.10	5.06	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-08	5.12	5.08	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-09	5.14	5.10	-0.040 0	0.78	3.08	3.09	0.010 0	0.32
2016-08-10	5.14	5.12	-0.020 0	0.39	3.08	3.08	0.000 0	0.00
2016-08-11	5.13	5.12	-0.010 0	0.19	3.07	3.08	0.010 0	0.32
2016-08-12	5.13	5.10	-0.030 0	0.58	3.08	3.08	0.000 0	0.00
平均值	5.12	5.09	0.032 9	0.64	3.08	3.08	0.008 6	0.28

[1]	MENG Xinyu, WANG Ruihan, ZHANG Xiping, WANG Mingjie, QIU Gang, WANG Zhengxia. Ultra-short-term wind power prediction based on empirical mode decomposition and multi-branch neural network [J]. Journal of Computer Applications, 2021, 41(1): 237-242.
[2]	Xiajie ZHANG, Jinghua ZHU, Yang CHEN. Distributed rough set attribute reduction algorithm under Spark [J]. Journal of Computer Applications, 2020, 40(2): 518-523.
[3]	Junhua GU, Feng WANG, Yongjun QI, Zheran SUN, Zepei TIAN, Yajuan ZHANG. Retrieval method of pulmonary nodule images based on multi-scale convolution feature fusion [J]. Journal of Computer Applications, 2020, 40(2): 561-565.
[4]	CUI Yixin, CHEN Xiaodong. Spark framework based optimized large-scale spectral clustering parallel algorithm [J]. Journal of Computer Applications, 2020, 40(1): 168-172.
[5]	CHENG Wenliang, WANG Zhihong, ZHOU Yu, GUO Yi, ZHAO Junfeng. Design of distributed computing framework for foreign exchange market monitoring [J]. Journal of Computer Applications, 2020, 40(1): 173-180.
[6]	LIU Jing, XIAO Guanfeng. Bus arrival time prediction system based on Spark and particle filter algorithm [J]. Journal of Computer Applications, 2019, 39(2): 429-435.
[7]	LIU Zihao, LI Ling, YE Feng. Anomaly detection method for hydrologic sensor data based on SparkR [J]. Journal of Computer Applications, 2019, 39(2): 436-440.
[8]	LI Longyang, DONG Yihong, SHI Weijie, PAN Jianfei. SQM: subgraph matching algorithm for single large-scale graphs under Spark [J]. Journal of Computer Applications, 2019, 39(1): 46-50.
[9]	ZHAO Wenfang, WANG Jingli, SHANG Min, LIU Yanan. Forecasting model of pollen concentration based on particle swarm optimization and support vector machine [J]. Journal of Computer Applications, 2019, 39(1): 98-104.
[10]	CUI Chen, ZHENG Linjiang, HAN Fengping, HE Mujun. Design of secondary indexes in HBase based on memory [J]. Journal of Computer Applications, 2018, 38(6): 1584-1590.
[11]	GU Junhua, HUO Shijie, WU Junyan, YIN Jun, ZHANG Suqi. Parallel multi-layer graph partitioning method for solving maximum clique problems [J]. Journal of Computer Applications, 2018, 38(12): 3425-3432.
[12]	FU Mou, YANG Hekun, WU Tangmei, HE Run, FENG Chaosheng, KANG Sheng. Fast video transcoding method based on Spark Streaming [J]. Journal of Computer Applications, 2018, 38(12): 3500-3508.
[13]	GU Junhua, WU Junyan, XU Xinyun, XIE Zhijian, ZHANG Suqi. Optimization and implementation of parallel FP-Growth algorithm based on Spark [J]. Journal of Computer Applications, 2018, 38(11): 3069-3074.
[14]	ZHAO Wenfang, LIU Xulin. Real-time processing system for automatic weather station data on Spark Streaming architecture [J]. Journal of Computer Applications, 2018, 38(1): 38-43.
[15]	HUANG Yakun, WANG Yang, WANG Mingxing. E-government recommendation algorithm combining community and association sequence mining [J]. Journal of Computer Applications, 2017, 37(9): 2671-2677.

Apple price prediction method based on distributed neural network

基于分布式神经网络的苹果价格预测方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 15

Related Articles 15

Recommended Articles

Metrics