Modeling and Predicting Total Electron Content by Semi-parametric Autoregressive(AR) Model

摘要：

Based on dynamic data system(DDS) modeling methodology, after transformed a seasonal time series for total electron content(TEC) data of the ionosphere into a stationary time series by differencing technique, stationary TEC values are modeled by the autoregressive(AR) model. In order to correct model’s systematic errors, authors proposed that AR model is improved by non-parameters introduced to AR model and the ionospheric TEC is predicted using the improved AR model which is called semi-parametric AR model. Preliminary results show that the semi-parametric AR model has a good performance than that of the AR model for short-term TEC prediction while, for relatively long-term TEC prediction, the performance of the semi-parametric AR model is not as good as that of AR model.

关键词： total electron content(TEC) modeling autoregressive(AR) model semi-parametric model prediction

作者: Xiuhai LI Dazhi GUO

作者单位: College of Surveying and Mapping EngineeringHeilongjiang Institute of TechnologyHarbin, China College of Geoscience and Surveying Engineering China University of Mining & Technology (Beijing) Be

会议类型: 国际会议

会议名称: 2011 International Conference on Information System and Computational Intelligence(2011 IEEE信息系统与计算智能国际会议 ICISCI 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 119-122

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）