统计与决策2018，Vol.34Issue(16)：18-21,4.DOI:10.13546/j.cnki.tjyjc.2018.16.004
基于神经网络与非参数核方法CPI的ARMA预测与非线性改进
CPI ARMA Prediction and Nonlinear Improvement Based on Neural Network and Non-parametric Kernel Method
摘要
Abstract
This paper takes Chinese CPI index sequence from January 1990 to January 2017 as the research object, adopts ARMA model to fit and predict the sequence, and obtains the short-term prediction error 3.599 and the long-term prediction error 12.528. Aiming at the defect that the ARMA model can not catch the nonlinear relation in CPI sequence, the paper uses BP neural network, RBF network and kernel method to improve it. The long-term prediction accuracy of the three models with nonlinear characteristics is comparable to that of the ARMA model, but the short-term prediction accuracy is greatly improved, with a maximum improvement of 51.85％.关键词
CPI/ARMA模型/BP网络/RBF网络/核方法Key words
CPI/ARMA model/BP neural network/RBF network/kernel method分类
管理科学引用本文复制引用
孙冠华..基于神经网络与非参数核方法CPI的ARMA预测与非线性改进[J].统计与决策,2018,34(16):18-21,4.
