## 1 Introduction
This 24 h empirical approach was implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users. The work of [PERSON] et al. (2023) provides an overview of most of the methods used for TEC forecasting: ANN, LSTM, LSTM-CNN, Encoder-Decoder LSTM, Extended ED-LSTME, NARX, conditional Generative Adversarial Network cGAN, and others, and gives statistical characteristics of these methods. In particular, for 1 h forecasting and up to 1-day and 2-day forecasting, the RMSE for a 1 h TEC forecast in low latitudes ranges from 2 to 5 TECU for different learning algorithms and different levels of solar activity. For the mid-latitude 1 h VTEC forecast, the RMSE is about 1.5 TECU. For a 1-day TEC forecast, the RMSE was 4 TECU in high solar activity and 2 TECU in low solar activity. However, noting the large complexity of using deep learning methods as a drawback of these methods, the authors used three algorithms: Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) for 1 h and 24 h VTEC forecasts. According to the authors, the advantages of these methods are that they are simple, non-parametric, fast to optimize, computationally efficient, and can be used on a limited dataset. The dataset for training and cross-validation included the period January 2015-December 2016. The test dataset included the period January-December 2017. Test results of six methods are given for three latitudes 70\({}^{\circ}\)N, 40\({}^{\circ}\)N, and 10\({}^{\circ}\)N (longitude not shown) and two periods: year 2017 and 7-10 September 2017. The results for the method showing the highest accuracy (Random Forest) are as follows. For 1 h ahead and latitude 70\({}^{\circ}\)N, the RMSE was 0.54 TECU, for 40\({}^{\circ}\)N the RMSE was 0.92 TECU, and for 10\({}^{\circ}\)N the RMSE was 1.2 TECU. For 24 h ahead and a latitude of 70\({}^{\circ}\)N, the RMSE was 1.06 TECU, for 40\({}^{\circ}\)N, the RMSE was 1.86 TECU, and for 10\({}^{\circ}\)N, the RMSE was 2.2 TECU. For the disturbed period 7-10 September 2017 and 1 h ahead, the RMSE were 0.73 TECU, 1.31 TECU, and 1.29 TECU for 70\({}^{\circ}\)N, 40\({}^{\circ}\)N, and 10\({}^{\circ}\)N, respectively. For 24 h ahead, these values were 1.77 TECU, 3.95 TECU, and 3.95 TECU. Thus, an analysis of the literature data shows that there is a certain sequence of using neural network methods LSTM to GRU to bidirectional to TCN, which allows gradually increasing the accuracy of the TEC forecast. However, the results can be highly dependent on the combination of architectures, region, and space weather conditions. In a previous work (2017), the following results for single hidden layer LSTM and gated recurrent unit neural networks were obtained for the Juliusruh station in 2015: MAE = 1.5 TECU, RMSE = 1.9 TECU, MAPE = 17% for GRU, MAE = 1.39 TECU, RMSE = 1.85 TECU, and MAPE = 14% for LSTM. In this paper, a set of multilayer neural network architectures, including both LSTM and GRU neural networks and temporal convolutional networks, are proposed and considered. The main aim of this paper is to complement them with bidirectional architecturesand to determine the method that provides the highest prediction accuracy in the European region. In Section 2, experimental data is described and the behavior of the basic indices of solar and geomagnetic activity and the total electron content is illustrated for the chosen years. Additionally, Section 2 presents the neural networks-based forecast methods. The results of the proposed methods are given in Section 3. Section 4 contains a discussion of the results. Section 5 provides the conclusions.