一种基于Hilbert-Huang变换和ARMA模型的时间序列预测方法

摘要

提出了一种基于Hilbert-Huang变换和ARMA模型的时间序列预测方法。采用Hilbert-Huang变换将原时间序列分解成若干个平稳的固有模态函数分量，求出每一个固有模态函数分量的瞬时频率和瞬时幅值，然后对每一个固有模态函数分量的瞬时频率和瞬时幅值序列建立ARMA模型，最后通过合成得到原时间序列的ARMA预测模型。实验结果表明，此方法可有效地应用于非平稳时间序列的预测。%A prediction method for time series based on Hilbert-Huang transform and ARMA model is proposed. The Hilbert-Huang transform is used to decompose the original time series into a number of intrinsic mode function components and the instantaneous frequencies and amplitudes of each intrinsic mode function component are obtained. Then the ARMA model of each instantaneous frequency and amplitude sequence is established. Finally,ARMA prediction model of the original sequence is obtained through compounding. Experimental examples demonstrate that the method based on Hilbert-Huang transform and ARMA model can be applied to predict non-stationary time series effectively.