Variable selection for multivariate time series prediction with neural networks

被引：0

作者：

Han, Min ^{[1
]}

Wei, Ru ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Elect & Informuat Engn, Dalian 116023, Peoples R China

来源：

NEURAL INFORMATION PROCESSING, PART I | 2008年 / 4984卷

关键词：

variable selection; neural network pruning; sensitivity; multivariate prediction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a variable selection algorithm based on neural networks for multivariate time series prediction. Sensitivity analysis of the neural network error function with respect to the input is developed to quantify the saliency of each input variables. Then the input nodes with low sensitivity are pruned along with their connections, which represents to delete the corresponding redundant variables. The proposed algorithm is tested oil both computer-generated time series and practical observations. Experiment results show that the algorithm proposed outperformed other variable selection method by achieving a more significant reduction in the training data size and higher prediction accuracy.

引用

页码：415 / 425

页数：11

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