Title :
Study of ionospheric TEC short-term forecast model based on combination method
Author :
Ruizhao Niu ; Chengjun Guo ; Yiran Zhang ; Liang He ; Yanling Mao
Author_Institution :
Res. Inst. of Electron. Sci. & Technol., UESTC, Chengdu, China
Abstract :
The ionospheric total electron content (TEC) is an important ionospheric parameters, Research of it has important significance on communication, radar, spaceflight, GNSS and other domains. Traditional short-term forecast model of ionospheric TEC uses single model so as to affects the prediction precision. A combination model based on seasonal model and ARMA model was put forward to overcome the shortages of traditional model. The TEC data of IGS in 2013 is used to analyze the two models. Prediction result shows the precision of combination model is superior to the traditional model.
Keywords :
autoregressive moving average processes; ionospheric techniques; total electron content (atmosphere); ARMA model; combination method; ionospheric TEC short-term forecast model; ionospheric total electron content; seasonal model; Accuracy; Analytical models; Correlation; Forecasting; Indexes; Predictive models; Time series analysis; combination model; ionospheric TEC; short-term forecast; time series;
Conference_Titel :
Signal Processing (ICSP), 2014 12th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-2188-1
DOI :
10.1109/ICOSP.2014.7015430
