| 基于双重预测模型的非线性时间序列预测 |
| |
| 引用本文： | 方勇,刘庆山.基于双重预测模型的非线性时间序列预测[J].系统仿真技术,2011,7(2):116-119,125. |
| |
| 作者姓名： | 方勇 刘庆山 |
| |
| 作者单位： | 上海大学 通信与信息工程学院,上海,200072 |
| |
| 基金项目： | 上海市重点学科资助项目（S30108） |
| |
| 摘 要： | 在支持向量机( SVM)预测问题中,为了减小错误参数选取对预测结果的影响,提出了1种基于双重预测模型的非线性时间序列预测算法.该算法在充分考虑支持向量机参数对推广能力影响的基础上,分别利用自回归预测模型(AR)、自回归滑动平均模型( ARMA)、线性回归和决策树模型对SVM参数进行预测,将预测参数运用到SVM预测模型中...
|
| 关 键 词： | 双重预测模型 支持向量机 自回归 火灾预测 非线性时间序列 |
Non-linear Prediction of Time Series Based on Dual-forecasting Model |
| |
| FANG Yong,LIU Qingshan.Non-linear Prediction of Time Series Based on Dual-forecasting Model[J].System Simulation Technology,2011,7(2):116-119,125. |
| |
| Authors: | FANG Yong LIU Qingshan |
| |
| Affiliation: | ( School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China) |
| |
| Abstract: | A new nonlinear time series prediction algorithm based on dual-forecasting model is proposed for support vector machine （SVM） to minimize the influence of error parameters. Fully utilizing the effect of the SVM parameter on generalization ability, we use Autoregressive （AR）, Autoregressive Moving Average （ARMA）, linear regression, and decision tree model to predict SVM parameter, followed by applying the predicted parameter to the SVM model. At last, fire occurred in China is forecasted by the algorithm, and numerical simulation demonstrates that the proposed method surpasses the Genetic Algorithm （GA） and Particle Swarm Optimization （PSO） based SVM in terms of prediction accuracy. |
| |
| Keywords: | dual-forecasting model support vector machine autoregression fire prediction non-linear time series |
| 本文献已被 维普 万方数据 等数据库收录！ |
|
