改进的多维灰色模型与支持向量机的组合预测

数学理论与应用 ›› 2018, Vol. 38 ›› Issue (3-4): 101-110.

改进的多维灰色模型与支持向量机的组合预测

梁志勋^1,3 ，袁泉³，曾祥艳²

1.河池学院计算机与信息工程学院
2.桂林电子科技大学数学与计算科学学院
3.桂林电子科技大学电子工程与自动化学院

出版日期:2018-12-30 发布日期:2020-09-21
基金资助:
国家自然科学基金项目(71801060)；

广西自然科学基金项目(2017GXNSFBA198182);

2019年度广西高校中青年教师科研基础能力提升项目(2019KY0622)

Combined Forecasting of Improved Multidimensional Grey Model and Support Vector Machine

Online:2018-12-30 Published:2020-09-21

摘要/Abstract

摘要： 支持向量机通过结构风险最小化原理来提高泛化能力,现多用于解决小样本的分类问题与回归问题,但用于预测时,单一的模型具有一定的局限性.本文提出一种将改进的多维灰色模型与支持向量机组合预测模型,实现不同模型的优势互补,避免单一模型的局限性,增加模型的稳定性.实验仿真结果表明,所提出的组合预测模型的预测效果明显优于支持向量机和基于新息优先累积法模型,且预测精度相对单一预测模型更高.

关键词: 预测模型, 多维灰色模型, 支持向量机, 组合预测模型

Abstract: Support vector machine improves the generalization ability through the principle of structural risk minimization.It is mostly used to solve the classification problem and regression problem of small samples.However,when used for prediction,a single model has certain limitations.In this paper,an improved multi-dimensional gray model and a support vector machine combined forecasting model are proposed.The combined forecasting model realizes the complementary advantages of different models,and can avoid the limitations of the single model,increase the stability of the model.The experimental simulation results show that the proposed combined forecast.The prediction effect of the model is significantly better than the support vector machine and the innovation-based prioritization method,the prediction accuracy of the combined prediction model is higher than that of the single prediction model.

Key words: Forecasting model, Multidimensional grey model, Support vector machine, The combined forecasting model

梁志勋, 袁泉, 曾祥艳 . 改进的多维灰色模型与支持向量机的组合预测[J]. 数学理论与应用, 2018, 38(3-4): 101-110.

Liang Zhixun, Yuan Quan, Zeng Xiangyan. Combined Forecasting of Improved Multidimensional Grey Model and Support Vector Machine[J]. Mathematical Theory and Applications, 2018, 38(3-4): 101-110.