多维时间序列的组合预测模型

doi:10.7523/j.issn.2095-6134.2016.06.015

摘要/Abstract

摘要：

由于时间序列在各领域的广泛应用，时间序列预测已经引起越来越多的关注，但关于多维时间序列的预测关注较少.然而，多维时间序列蕴含着丰富的信息.针对该问题，提出基于k近邻（k-nearest neighbor，k-NN）和BP神经网络的多维时间序列组合预测模型.首先分别采用k-NN和BP神经网络进行预测，得到对应的预测结果.然后使用BP神经网络进行非线性组合，得到最终的预测结果.实验表明，该预测模型优于k-NN和BP神经网络预测模型.

关键词: 多维时间序列预测, k-NN, BP神经网络

Abstract:

Time series prediction has attracted more and more attention due to its applications in various areas, but scant attention has focused on multivariate time series prediction. By using multi-dimensional time series one can get more information about the system. Considering this problem, we propose a combined prediction model for multi-dimensional time series based on k-NN and BP neural network. We use k-NN and BP neural network to get two prediction results, respectively. Then we reuse BP neural network to get the final prediction results. The experimental results show that the proposed method outperforms k-NN and BP neural network in prediction.

Key words: multi-dimensional time series prediction, k-NN, BP neural network

中图分类号:

TP311.13

赵亚伟, 陈艳晶. 多维时间序列的组合预测模型[J]. 中国科学院大学学报, 2016, 33(6): 825-833.

ZHAO Yawei, CHEN Yanjing. A combined prediction model for multi-dimensional time series[J]. , 2016, 33(6): 825-833.

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