Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region
Artem Kharakhashyan,
Olga Maltseva
Abstract:Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and Temporal Convolutional Networks. The set of methods included 10 architectures: TCN, modified LSTM/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is perform… Show more
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