ED-iSwinLSTM-ND: A Spatiotemporal Deep Learning Model for Global Ionospheric TEC Forecasting

Accurate forecasting of global ionospheric total electron content (TEC) is important for space weather research and Global Navigation Satellite System (GNSS) applications. To address the complex spatiotemporal variability of TEC and the artificial discontinuity across the ±180° longitude boundary in global TEC maps, this study proposes an improved SwinLSTM model (iSwinLSTM) and further develops the ED-iSwinLSTM-ND framework for global TEC forecasting. By redesigning the shifted-window mechanism to account for longitudinal periodicity, iSwinLSTM enhances cross-boundary information interaction, while an Encoder–Decoder architecture combined with the Next-Day (ND) strategy enables prediction of the next 12 TEC maps from the preceding 36 maps. The model was evaluated retrospectively using archived GIMs from the selected test years. In 2025, a high solar activity year, the model achieved an MAE of 3.51 TECU, an RMSE of 5.10 TECU, and an R2 of 0.94; in 2020, a low solar activity year, the corresponding values were 0.96 TECU, 1.50 TECU, and 0.95. Boundary experiments further confirmed that the periodic shifted-window mechanism effectively reduces longitude-boundary errors. Overall, ED-iSwinLSTM-ND provides an effective framework for short-term global TEC forecasting by jointly exploiting spatiotemporal dependencies, longitudinal periodicity, and diurnal characteristics.