## 3 Results  
The statistical results on accuracy of TEC forecasting for a set of 10 considered architectures and the two sets of indices (including and excluding Vsw) are presented in Table 1.  
The metrics considered were MAE, MAPE, RMSE, and \(\rho\), averaged over half a year during single step 2 h ahead TEC forecasting for three years separately and four selected stations. The results for station Juliusruh are presented in Table 1 for 2015. The results for every station considered are shown in Figure 11 as a dependence of MAE, MAPE, and RMSE on the architecture's variant number. Thus, the results are presented for the following architectures, indicated by numbers: (1) LSTM-FRF, (2) LSTM-RFF, (3) GRU-FRF, (4) GRU-RFF, (5) TCN, (6) BiLSTM-FRF, (7) BiLSTM-RFF, (8) BiGRU-FRF, (9) BiGRU-RFF, and (10) BiTCN. Due to the fact that there is no unambiguous trend in the influence of the Vsw index on the accuracy of the forecast, only the option with the best result (with and without Vsw) was chosen from the two options. From Figure 11, it can be seen that among the prediction accuracy metrics, three sections can be distinguished with similar characteristics, depending on the architectures used. The first flat area (numbers 1-5) with the highest values for errors refers to traditional LSTM and GRU architectures with FRF and RFF modifications. The second section (numbers 6-8), which includes results for bidirectional networks, has a minor slope and error values are 1.5-2 times smaller compared to the first section. Finally, the third section, which includes BiGRU and BiTCN architectures, provides the lowest error values, i.e., the highest forecasting accuracy. It should be noted that although there is a certain latitude dependence of the statistical characteristics in the first two groups, for the BiTCN architecture, it practically ceases to exist. \begin{table}
\begin{tabular}{c l c c c c} \hline \hline  & **Architecture and Indices** & **MAE (TECU)** & **MAPE (\%)** &
\begin{tabular}{c} **RMSE** \\ **(TECU)** \\ \end{tabular} & \(\rho\) \\ \hline  
CN(10 Kp, Np, F10.7, Dst) & 0.191 & 2.615 & 0.555 & 0.996 \\ \hline
20 & BiTCN(10 Kp, Np, F10.7, Dst, Vsw) & 0.161 & 2.192 & 0.507 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forecasting results for Juliusruh station in 2015. It can be seen that MAPE and RMSE are related to the absolute values of TEC, depending on the level of solar activity. Conventional LSTM and GRU with FRF and RFF modifications and conventional TCN provide results at the same level of accuracy. The usage of bidirectional architecture essentially improves the accuracy of the forecast for almost all architectures and across all stations. It should be noted that during high solar activity, bidirectional architectures of the FRF line (BiLSTM-FRF and BiGRU-FRF) do not improve the accuracy of the forecast for a low-latitude station even compared to conventional methods. The highest accuracy is achieved using BiGRU-RFF and BiTCN architectures: MAPE values are less than 0.6 TECU, RMSE values are less than 0.8 TECU, and MAPE values are less than 7.5%. The best forecasting accuracy is achieved using BiTCN architecture, with less than 0.3 TECU for MAE, less than 0.6 TECU for RMSE, and less than 5% for MAPE. The forecast errors during the second half of each year for the Moscow station are shown in Figure 12. Each panel for the 2015, 2020, and 2022 years shows the variations of TEC between DoY 185 and 365 along with the Dst index and compares the prediction accuracy for two architectures: LSTM-FRF and BiTCN. All the cases are provided with the Vsw index included in the training data (10 Kp, Np, F10.7, Dst, and Vsw). Figure 11: Statistical characteristics of TEC forecast in the European zone depending on a combination of architectures and indices for 2015 (**a–c**), 2020 (**d–f**), and 2022 (**g–i**). Figure 12 shows the instantaneous TEC and \(\delta\)TEC values for the two selected architectures from among the unidirectional and bidirectional ones, using the Moscow station as an example.