Ionospheric TEC Prediction Model Based on LSTM-SpatioTemporal Transformer

However, traditional statistical models exhibit significant limitations in handling the high noise, non-stationarity, and complex dynamic characteristics of TEC data. To address this issue, this study proposes a hybrid prediction model combining a SpatioTemporal Transformer (STT) and a long short-term memory (LSTM) network, with the incorporation of attention-weighted auxiliary predictors.

In an experimental setting for ionospheric TEC prediction over China and its surrounding regions, data from 2000 to 2022 were used for training, while data from the solar maximum year 2023 served as the test set. The study focused on evaluating the impact of different parameter combinations on TEC prediction performance under varying ionospheric conditions. Ablation experiments demonstrate that the proposed hybrid model outperforms single models.