基于Huang变换和ARIMA模型的时间序列预测方法
Prediction of Time Series Based on Huang Transform and ARIMA Model
文中首先通过Huang变换将非平稳时间序列分解为有限个固有模态函数和一个残余函数之和，然后应用ARIMA模型对各个固有模态函数和残余函数进行预测，最后将所有的预测值重构叠加，得到原始时间序列的预测值。实例证明，基于Huang变换和ARIMA模型的时间序列预测方法，优于小波变换、ARIMA模型以及小波变换和ARIMA模型相结合的预测方法，提高了预测精度。
his paper firstly decomposed the non-stationary time series into a finite and often small number of Intrinsic Mode Functions (IMF) and one Remnant Function (RF). Secondly, ARIMA model is applied to predict IMF and RF. Experiment results illustrate that the new predicting method is better than wavelet analysis, ARIMA model, and wavelet analysis with ARIMA model and it improves the forecasting accuracy.
田富鹏、马亮亮
计算技术、计算机技术自动化基础理论
Huang变换RIMA模型时间序列预测固有模态函数
Huang transformARIMA modeltime seriespredictionintrinsic mode function
田富鹏,马亮亮.基于Huang变换和ARIMA模型的时间序列预测方法[EB/OL].(2012-11-09)[2025-08-04].http://www.paper.edu.cn/releasepaper/content/201211-144.点此复制
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