## 2 Materials and Methods
### Experimental Data and TEC Behavior  
The comparison of the forecasting accuracy of TEC using recurrent and convolutional neural networks, including conventional and bidirectional implementations, was carried out for 3 selected years with significantly different geomagnetic conditions and solar activity levels. The values of global JPL GIM-TEC maps were calculated from IONEX files with a time step of 2 h ([[https://urs.earthdata.nasa.gov](https://urs.earthdata.nasa.gov)]([https://urs.earthdata.nasa.gov](https://urs.earthdata.nasa.gov)) (accessed on 10 February 2023)) for Juliisruh (54.6\({}^{\circ}\)N, 14.6\({}^{\circ}\)E), Murmansk (69\({}^{\circ}\)N, 33\({}^{\circ}\)E), Moscow (55.6\({}^{\circ}\)N, 37.2\({}^{\circ}\)E), and Nicosia (35.1\({}^{\circ}\)N, 33.2\({}^{\circ}\)E) for three years 2015, 2020, and 2022. The data on the indices of solar and geomagnetic activity, Dst, F10.7, proton density Np, planetary 10 Kp index, and speed of solar wind Vsw, was taken from SPDF OMNIWeb Service ([[http://omniweb.gsfc.nasa.gov/form/dx1.html](http://omniweb.gsfc.nasa.gov/form/dx1.html)]([http://omniweb.gsfc.nasa.gov/form/dx1.html](http://omniweb.gsfc.nasa.gov/form/dx1.html)) (accessed on 10 February 2023)). The state of the ionosphere and the dynamics of the total electron content depend on the level of solar and geomagnetic activity. The behavior of the basic indices influencing the TEC changes is given in Figure 1 for the years chosen for the analysis: 2015 (near to a maximum of 24 solar cycle), 2020 (a minimum of 24 cycle and transition to 25 cycle), and 2022 (an ascending branch of 25 cycle). Figure 1: _Cont._  
There is a clear downward trend in F10.7 in 2015, a persistence in 2020 with a rather sharp increase at the end of the year, and an increase in 2022. The maximum disturbance was observed in 2015 and the minimum in 2020. Figure 2 shows the median values, TEC(med), for the Juliusruh station (left panels) and stations along the 30\({}^{\circ}\)E meridian (right panels). Figure 1: Average daily values of F10.7 and Dst indices for different years: (**a,b**) 2015; (**c,d**) 2020; (**e,f**) 2022. Figure 2: Variations of medians, TEC(med), for the reference station, Juliusruh, and stations along the meridian 30\({}^{\circ}\)N: (**a,b**) 2015; (**c,d**) 2020; (**e,f**) 2022. The year 2015 is on the descending branch of the 24 th cycle, which determines higher TEC values in the first half of the year compared to the second half. The year 2020 falls on the minimum of solar activity and is characterized by a more uniform seasonal course and 2-3 times lower maximum values compared to the year 2015. The year 2022 is on the rising branch of cycle 25, and the seasonal dependence differs from the first two cases both in the dynamics of change and in the maximum values of TEC. For stations along the meridian 30\({}^{\circ}\)E, this trend is preserved with the corresponding ratio between the maximum values, and this will be reflected in the MAE and RMSE values for different forecast architectures. The dynamics of TEC(med) for the Juliusruh station shows trends determined by the course of the F10.7 index. A comparison of the dynamics of TEC(med) along the meridian shows that for any considered level of solar activity, TEC predominantly increases with decreasing latitude. As noted above, many papers show that taking indices into account allows an increase of the accuracy of TEC prediction. Calculation of the correlation coefficients of TEC with these indices showed that in this case, the most significant were the Dst and solar wind speed Vsw indices. The correlation coefficients are shown in Figure 3.  
The most commonly used indices are F10.7 and Dst. In this work, indices F10.7, Dst, 10 Kp, Np, and Vsw were used separately and together. It can be seen that these two factors can influence in phase or in antiphase. Figures 4 and 5 show that the influence of Dst, which led to a negative response, was stronger than that of Vsw for June 2015.