Knowledge Commons of Institute of Automation,CAS
Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by Web crawler | |
Weng, Yuchen1,2; Wang, Xiujuan1,2; Kang, Mengzhen1,3; Hua, Jing1,3; Wang, Fei-yue1,4,5 | |
发表期刊 | IEEE Transactions on Computational Social Systems |
2019-06 | |
卷号 | 6期号:3页码:547-553 |
摘要 | The sales of agricultural products is an important component of product supply chain. The price of agricultural products, a social signal of product supply and demand, is affected by many factors, such as climate, price, policy, etc. Due to the asymmetry between production and marketing information, the price of many agricultural products fluctuates greatly. Horticultural products are especially sensitive to price since they are not suitable for long-term storage. Therefore, forecasting the price of horticultural products is very helpful in designing cropping plan. In this paper, ARIMA model, BP network method and RNN method were tested to forecast the price of agricultural products (cucumber, tomato and eggplant) in short-term (several days) and long-term (several weeks or months). A large-scale price data of agricultural products were collected from the website based on web crawler technology. Since ARIMA requires continuous and periodic data, it is suitable for small-scale periodic data. It gave good performance for average monthly data, but not for daily data. Instead, the neural network methods(including BP network and RNN) can predict well daily, weekly and monthly trend of price fluctuation. It is more suitable for large-scale data. It is expected that the deep learning method represented by neural network will become the mainstream method of agricultural product price forecasting. |
关键词 | Web crawler cucumber agricultural CPSS framework ARIMA model neural network price forecasting |
收录类别 | EI |
七大方向——子方向分类 | 人工智能+农业 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39035 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Weng, Yuchen |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.Beijing Engineering Research Center of Intelligent Systems and Technology, Beijing 100190, China 3.Innovation Center for Parallel Agriculture, Qingdao Academy of Intelligent Industries, Qingdao 266109, China 4.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China 5.Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Weng, Yuchen,Wang, Xiujuan,Kang, Mengzhen,et al. Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by Web crawler[J]. IEEE Transactions on Computational Social Systems,2019,6(3):547-553. |
APA | Weng, Yuchen,Wang, Xiujuan,Kang, Mengzhen,Hua, Jing,&Wang, Fei-yue.(2019).Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by Web crawler.IEEE Transactions on Computational Social Systems,6(3),547-553. |
MLA | Weng, Yuchen,et al."Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by Web crawler".IEEE Transactions on Computational Social Systems 6.3(2019):547-553. |
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