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在充分考虑乘积性季节模型的情况下,采用时间序列分析中的求和自回归移动平均模型(autoregressive integrated moving average,ARIMA)对TEC值序列进行预报分析。以欧洲定轨道中心(CODE)提供的2008—2012年电离层TEC值为样本数据,重点分析该方法在不同电离层环境(电离层平静期和活跃期)和不同纬度下的预报性能以及影响该方法预报精度的因素分析等。试验结果表明:在预报精度方面,电离层平静期和活跃期预报6d的平均相对精度可达83.3%和86.6%;而平均预报残差分别为0.18±1.9TECU和0.69±2.6TECU,其中预报残差小于3TECU分别达到90%和81%以上;而且两个时期都具有纬度越高相对精度越低而绝对精度越高的规律。在影响因素方面,预报精度会随TEC样本序列长度增加而提高,但随着样本序列增加到一定值(约30d左右)后,其相对精度提高不大;而相同样本数据的预报精度则会随预报长度的增加而减小,初期并不明显,但超过30d其相对精度将随时间明显降低。
Taking full account of the product seasonal model, the TEC value sequence was predicted and analyzed by ARIMA in time series analysis. Based on the 2008-2012 ionospheric TEC values provided by the European Orbital Center (CODE) as sample data, we focus on the prediction performance and influence of this method under different ionospheric environments (ionospheric quiet and active periods) and at different latitudes Method of forecasting accuracy factor analysis. The experimental results show that the average relative accuracy of the forecasting period of 6 days is 83.3% and 86.6% respectively, while the average forecast residuals are 0.18 ± 1.9TECU and 0.69 ± 2.6TECU, respectively. The forecast residuals The difference is less than 3TECU respectively 90% and 81%; and both periods have the higher latitude the lower the relative accuracy and the absolute accuracy of the law. As for the influencing factors, the accuracy of the prediction increases with the increase of the length of the TEC sample sequence, but its relative accuracy does not increase much as the sample sequence increases to a certain value (about 30 days). However, the prediction accuracy of the same sample data will vary with However, the relative accuracy over 30 days will be significantly reduced over time.