One-step ahead prediction of f oF2 using time series forecasting techniques

In this paper the problem of one-step ahead prediction of the critical frequency (f oF2) of the middlelatitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58 000 observations of f oF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38 N, 23.5 E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the k-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of f oF2.