首页 | 本学科首页   官方微博 | 高级检索  
     检索      

非平稳时间序列分析的WAVELET—改进GM(1,1)组合方法及其应用
引用本文:张贵杰,任永泰,王福林,付强.非平稳时间序列分析的WAVELET—改进GM(1,1)组合方法及其应用[J].数学的实践与认识,2012,42(1):135-140.
作者姓名:张贵杰  任永泰  王福林  付强
作者单位:1. 东北农业大学工程学院,黑龙江哈尔滨,150030
2. 东北农业大学理学院,黑龙江哈尔滨,150030
3. 东北农业大学水利与建筑学院,黑龙江哈尔滨,150030
基金项目:黑龙江省青年基金“哈尔滨市及城近郊区地下水水位水质预警研究”(QC08C64)
摘    要:提出非平稳时间序列分析的WAVELET—改进GM(1,1)组合方法.首先利用Mallat算法对非平稳时间序列进行小波分解;然后采用能量阈值选择策略对高频系数进行处理,并将其与低频系数进行小波重构;最后运用改进的GM(1,1)方法预测.该方法不仅能充分拟合低频信息,而且可避免对高频信息的过拟合.实验结果证明,该方法比传统的非平稳时间序列预测方法具有更高的预测精度.

关 键 词:小波分解  非平稳时间序列  能量阈值  改进GM(1  1)模型  预测

Wavelet-improved GM(1,1) Method in the Non-stationary Time Series and Its Application
ZHANG Gui-jie , REN Yong-tai , WANG Fu-lin , FU Qiang.Wavelet-improved GM(1,1) Method in the Non-stationary Time Series and Its Application[J].Mathematics in Practice and Theory,2012,42(1):135-140.
Authors:ZHANG Gui-jie  REN Yong-tai  WANG Fu-lin  FU Qiang
Institution:1.College of Engineering,Northeast Agricultural University,Harbin 150030,China) (2.College of Science,Northeast Agricultural University,Harbin 150030,China) (3.College of Water Conservancy and Building Engineering,Northeast Agricultural University,Harbin 150030, China)
Abstract:In this paper,WAVELET—improved GM(1,1) Method is proposed in the nonstationary time serious.By wavelet decomposition,the non-stationary time series were decomposed. Then the energy threshold selection strategy was adopted to process the high frequency coefficients.By wavelet transform,make the processed high frequency coefficients and low-frequency coefficients reconstruct.Use the improved GM(1,1) method to make forecast. This new method avoids the over-fitted for high frequency signals,and adequately fits the low singal of the non-stationary time series,so better predicting performance can be obtained. Experiments show the novel method is higher accuracy in comparison with the traditional one.
Keywords:wavelet decomposition  non-stationary time series  energy threshold  improved GM(1  1) model  prediction
本文献已被 CNKI 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号