基于EWT-ARMA的短期电离层TEC预测模型

Short-Term Ionospheric TEC Prediction Model Based on EWT-ARMA

针对电离层总电子含量(TEC)数据非线性、非平稳的特点,在自回归移动平均(ARMA)模型的基础上,结合经验小波变换(EWT),提出一种组合的短期电离层预测方法。采用IGS提供的电离层TEC格网数据进行实验,通过对比分析可知,相较于单一ARMA模型,本文组合模型在太阳活动低年和太阳活动高年5 d内的平均相对精度分别提高4.8%和2.8%,前1 d内组合模型的平均相对精度分别提高7%和6.1%。

In view of the non-linear and non-stationary characteristics of the ionospheric total electron content(TEC)data,based on the auto-regressive moving average(ARMA)model prediction method and the empirical wavelet transform(EWT)method,we propose a combined short-term ionospheric prediction method.Using the ionospheric TEC grid data provided by IGS,the results show that,compared with the average relative accuracy of single ARMA model,the average relative accuracy of the combined model in low solar activity and high solar activity years is increased by 4.8%and 2.8%respectively,and the average relative accuracy of the combined model in the first day is increased by 7%and 6.1%.