| 基于改进支持向量机的林业资金投资预测方法 |
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| 引用本文： | 陶佳,沈红岩,高冠东.基于改进支持向量机的林业资金投资预测方法[J].河北农业大学学报,2012,35(4):123-126. |
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| 作者姓名： | 陶佳 沈红岩 高冠东 |
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| 作者单位： | 1. 河北农业大学信息科学与技术学院,河北保定,071000
2. 中央司法警官学院信息管理系,河北保定,071000 |
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| 摘 要： | 针对林业资金投资变化的定量预测,提出一种基于改进支持向量机的预测方法.利用滑动时间窗口方法将历年林业资金投资数据构造成时间序列,将其做为数据样本集并由改进支持向量机加以训练以得到预测模型.通过某省近20年的林业资金投资数据实验验证了预测方法的有效性,实验结果表明:与传统预测方法相比,基于改进支持向量机的预测方法明显提高了投资变化预测精度.
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| 关 键 词： | 林业资金投资 回归预测 时间序列 支持向量机 粒子群算法 |
A novel forestry investment forecast method based on improved support vector machine |
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| TAO Jia
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SHEN Hong-yan
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GAO Guan-dong.A novel forestry investment forecast method based on improved support vector machine[J].Journal of Agricultural University of Hebei,2012,35(4):123-126. |
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| Authors: | TAO Jia SHEN Hong-yan GAO Guan-dong |
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| Institution: | 1.College of Information Science and Technology,Agricultural University of Hebei,Baoding 071000,China; 2.Department of Information and Management,Central Institute for Correctional Police,Baoding 071000,China) |
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| Abstract: | Focused on forecast investment forecast,a novel method based on improved support vector machine with particle swarm optimization(SVM-PSO) was proposed,in order to improve forecast accuracy.With sliding time window,history of forestry investment data had been constructed into a time series.In order to obtain forecast model,the time series was trained as sample set by SVM-PSO.Finally,the experiments,whose data is from the forestry investment of nearly 20 years,show that the SVM-PSO forecast method has better performance than the traditions. |
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| Keywords: | forestry investment forecast regression forecast time series support vector machine particle swarm optimization |
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