应用半参数AR模型的电离层TEC建模与预测

Prediction of ionospheric Total Electron Content based on semiparametric autoregressive model

本文基于时间序列分析的DDS(Dynamic Data System)建模法,对季节性的电离层总电子含量(TEC)时间序列观测值平稳化后建立自回归AR模型,提出以半参数AR模型对普通AR模型精化,并利用半参数AR模型对电离层TEC预报。实例分析表明:利用半参数AR模型对电离层TEC进行预报,在短期内半参数模型预报效果优于普通AR模型,但随着预报时间变长,则半参数模型预报精度明显下降,其预报效果则不如普通的AR模型。

Based on Dynamic Data System（DDS） modeling methodology, after transforming a seasonal time series for ionospheric Total Electron Content（TEC） into a stationary time series by seasonal differences and regular differences, stationary TEC values were modeled by the autoregressive（AR） model in the paper. In order to correct the systematic errors, the authors proposed that AR model could be refined by introducing semiparameters to AR model and the ionospheric TEC could be predicted using the semi-parametric AR model. The preliminary results showed that the semiparametric AR model had a good performance than the AR model in short-term TEC prediction, however, for relatively long-term TEC prediction, the performance of the semiparametric AR model would be no less than that of AR model.