Global Ionospheric Total Electron Content Forecasting Model Using Temporal Convolutional Network

The ionosphere is a crucial component of Earth's atmosphere, with significant impacts on radio transmission, satellite navigation, radar surveillance, and other applications. Total Electron Content (TEC) in the ionosphere is one of the vital parameters used to describe ionospheric activity and characteristics. Due to limited observational methods, we often need the TEC predictions based on historical TEC data. In this study, we employ the Temporal Convolutional Network (TCN) architecture to construct a global ionospheric TEC prediction model. We compare the TEC predictions from the TCN model under different ionospheric activity conditions with those from time-series models and deep learning recurrent structure models. The research findings indicate that the TCN-based model offers higher prediction accuracy and greater stability for global ionospheric TEC forecasts.