Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting

Abstract Accurate forecasting of the ionospheric state is crucial for various applications including remote sensing and navigation. Total electron content (TEC) is an important ionospheric parameter to reflect ionospheric state. Consequently, there is a great interest in the prediction of TEC. In this study, we integrated the Informer deep learning algorithm and Principal Component Analysis (PCA), a dimensionality reduction technique, to achieve spatio‐temporal modeling for forecasting the global TEC maps. Our evaluation, based on test set data from 2015 to 2022, demonstrate that the PCA‐Informer model outperforms the IRI‐2016, standalone Informer, and PCA‐enhanced Long Short‐Term Memory (PCA‐LSTM) models in terms of accuracy with root mean squared error (RMSE) of 2.60 TECU and mean relative error (MRE) of 14.1%, and stability for predicting TEC maps for the subsequent 2 days. Two distinct periods (a geomagnetic quiet period and a strong storm period) in 2018 have been selected for evaluating the models' efficacy. The PCA‐Informer model has shown remarkable predictive precision during the initial and main phase of strong geomagnetic storms as well as quiet period. During 132 geomagnetic storm events on the test set, the PCA model exhibits RMSE of 3.6 TECU and MRE of 17.7%, which are lower than IRI‐2016 (6.1 TECU, 33.38%), PCA‐LSTM (4.5 TECU, 21.52%) and Informer (4.1 TECU, 20.64%). Additionally, model errors are negatively correlated with the minimum Dst, while PCA‐Informer has the best robustness.