A long time-series forecasting informer architecture-based ionospheric foF2 model in the low-latitude region

Deep learning models have made great accomplishments in space weather forecasting. The critical frequency of the ionospheric F2 layer (foF2) is a key ionospheric parameter, which can be understood and predicted by some advanced new deep learning technologies. In this paper, we utilized an Informer architecture model to predict foF2 for several hours up to 48 h and analyzed its variations during periods of quiet, moderate, and intense geomagnetic conditions. The Informer method forecasts the temporal variations of foF2 by processing and training the past and present foF2 data from the Haikou station, China, during 2006-2014. It is evident that the Informer-foF2 model achieves better prediction performance than the widely used long short-term memory model. The Informer-foF2 model captures the correlation features within the foF2 time series and better predicts the variations ranging for hours up to days during different geomagnetic activities.