基于奇异谱分解的玉米价格多尺度组合预测

张大斌 陈政希 黄玉宣 王楠 林婉 潘镇飞

摘要为提高玉米价格预测精度，基于分解-重构-集成思想，构建一个基于奇异谱分解的多尺度组合模型。首先对原始序列进行奇异谱分解，并用对角平均法将分量序列重构，用作单个模型的输入，最后用BP神经网络对各单一模型输出进行非线性集成。对比分析了分别将原始序列、重构序列作为输入，各单一模型和多尺度组合模型的预测效果。结果表明，该研究所建模型要优于各单一模型。基于价格总体趋于平稳的情况，政府应结合实际情况适当采取措施以保障玉米价格的持续稳定。

关键词玉米价格;奇异谱分解;多尺度模型;组合预测

中图分类号S-9文献标识码A

文章编号0517-6611（2020）15-0241-06

doi：10.3969/j.issn.0517-6611.2020.15.068

开放科学（资源服务）标识码（OSID）：

Multiscale Combination Forecast of Corn Price Based on Singular Spectrum Decomposition

ZHANG Dabin，CHEN Zhengxi，HUANG Yuxuan et al

（School of Mathematics and Information （School of Software）， South China Agricultural University，Guangzhou，Guangdong 510642）

AbstractIn order to improve the accuracy of corn price forecasting， a multiscale combination model based on singular spectral decomposition is constructed based on the decompositionreconstructionintegration idea.First， singular spectral decomposition is performed on the original sequence， and the component sequence is reconstructed using the diagonal average method as the input of a single model. Finally， the BP neural network is used to integrate the outputs of each single model nonlinearly. The prediction effect of each single model and multiscale combination model was compared and analyzed using the original sequence and the reconstructed sequence as inputs.The results showed that the model built in this paper is better than each single model.Based on the fact that prices are generally stable， the government should take appropriate measures in accordance with actual conditions to ensure the continued stability of corn prices.

Key wordsCorn price;Singular spectrum decomposition;Multiscale model;Combined forecast

基金項目广东省自然科学基金项目（2016A030313402）;大学生创新创业项目（201810564106）。

作者简介张大斌（1969—），男，湖北潜江人，教授，博士生导师，从事预测理论与方法研究。

收稿日期2019-12-03;修回日期2020-01-09