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In this paper the problem of one-step ahead prediction of the criticalfrequency (foF2) of the middle-latitude ionosphere, using time seriesforecasting methods, is considered. The whole study is based on a sample ofabout 58000 observations of foF2 with 15-min time resolution, derivedfrom the Athens digisonde ionograms taken from the Digisonde Portable Sounder(DPS4) located at Palaia Penteli (38° N, 23.5° E), for the periodfrom October 2002 to May 2004. First, the embedding dimension of thedynamical system that generates the above sample is estimated using the falsenearest neighbor method. This information is then utilized for the trainingof the predictors employed in this study, which are the linear predictor, theneural network predictor, the persistence predictor and the k-nearestneighbor predictor. The results obtained by the above predictors suggestthat, as far as the mean square error is considered as performance criterion,the first two predictors are significantly better than the latter twopredictors. In addition, the results obtained by the linear and the neuralnetwork predictors are not significantly different from each other. This maybe taken as an indication that a linear model suffices for one step aheadprediction of foF2

One-step ahead prediction of &lt;i&gt;fo&lt;/i&gt;F2 using time series forecasting techniques

One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques