Abstract: With a fully consideration of the Multiplicative Seasonal model, we transform a seasonal time series for ionspheric Total Electron Content (TEC) into a stationary time series by seasonal differences and regular differences firstly, then use Autoregressive Integrated Moving Average (ARIMA) model from the time series analysis theory to model the stationary TEC values so as to predict the TEC series. Using the TEC data from 2008 to 2012 provided by the Center for Orbit Determination in Europe (CODE) as sample data, we analysed the precision of this method in the prediction of ionosphere TEC value which varies from high latitude to low latitude in both quiet and active period. The effect of TEC sample’s length on the predicted accuracy is analyzed, too. Results from numerical experiments show that in ionospheric quiet period the average relative prediction accuracy of 6 days are up to 83.3% with an average prediction residual errors of about 0.18±1.9TECu, while it changes to 86.6% with an average prediction residual errors of about 0.69±2.6TECu in ionospheric active period. For the former, above 90% of predicted residual is less than ±3TECu while the latter is only about 81%. Two periods show the same law that the higher the latitude, the higher the absolute precision, and the lower the predicted relative accuracy. In addition, the prediction accuracy will improve with the increase of TEC sample sequences length, but it will gradually reduce if the length exceed the optimal length about 40 days. On the other hand, with the same TEC sample, the predicted days increase, the predicted accuracy decreases. Though it’s not obvious in the beginning, it will be significantly reduced over 30 days.
