A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

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• 作者：Haoyang Wu ; Huaili Wu ; Minfeng Zhu ; Weifeng Chen ; Wei Chen
• 关键词：Change warning ; Mixed model ; Neural networks ; Price forecasting
• 刊名：Journal of Big Data
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：4
• 期：1
• 全文大小：1908KB
• 刊物类别：Database Management; Information Storage and Retrieval; Data Mining and Knowledge Discovery; Computa
• 刊物主题：Database Management; Information Storage and Retrieval; Data Mining and Knowledge Discovery; Computational Science and Engineering; Mathematical Applications in Computer Science; Communications Engine
• 出版者：Springer International Publishing
• ISSN：2196-1115
• 卷排序：4

文摘

In order to forecast prices of arbitrary agricultural commodity in different wholesale markets in one city, this paper proposes a mixed model, which combines ARIMA model and PLS regression method based on time and space factors. This mixed model is able to obtain the forecasting results of weekly prices of agricultural commodities in different markets. Meanwhile, this paper sets up variables to measure the price changing trend based on the change of exogenous variables and prices, thus achieves the warning of daily price changes using neural networks. The model is tested with the data of several types of agricultural commodities and error analysis is made. The result shows that the mixed model is more accurate in forecasting agricultural commodity prices than each single model does, and has better accuracy in warning values. The mixed model, to some extent, forecasts the daily price changes of agricultural commodities.