## 5 Results of the Study
### Best Results Achieved  
The 10 most successful studies according to MAE, RMSE, and R\({}^{2}\)_score indices are listed in Table 7. In all these experiments, the epoch value was taken as 50. The most successful study was obtained with the BiLSTM model, the ReLU activation function, and the Adam optimization function. In the study, a value of 0.9976 was reached for R\({}^{2}\). Another study using the same dataset reached an R\({}^{2}\) value of 0.9913, which was presented as the highest value obtained [53]. Another study using the same dataset reached an R\({}^{2}\) value of 0.98 [54]. In a similar study, applying random hyperparameter optimisation to the LSTM model reduced the error rate to below 1.5% [52]. The graph of the real and predicted values of the first 200 and 30,000 values of this model can be seen in Figure 15a,b. Figure 16 shows how close the predictions are to the real value line. In other models, the success ranking decreases from the highest to the lowest.  
\begin{table}
\begin{tabular}{c c c c c c|c} \hline
**Model** & **Layers** & **Activator** & **Optimizer** & **Epochs** & **test\_rmse** & **test-mae** & **test\_R\({}^{2}\)** \\ \hline BiLSTM & 50 & relu & Adam & 50 & 313.04 & 223.66 & 0.9976 \\ LSTM & 50 & relu & Adam & 50 & 339.33 & 242.54 & 0.9972 \\ BiLSTM & 50 & relu & Nadam & 50 & 345.03 & 255.65 & 0.9971 \\ BiLSTM & 50 & Leakyrelu & Adam & 50 & 345.7 & 255.37 & 0.9971 \\ LSTM & 50 & elu & [PERSON] & 50 & 345.19 & 249 & 0.997 \\ LSTM & 50 & tanh & [PERSON] & 50 & 348.22 & 248.38 & 0.997 \\ LSTM & 50 & Leakyrelu & [PERSON] & 50 & 356.31 & 257.81 & 0.9969 \\ SimpleRNN & 50 & relu & [PERSON] & 50 & 368.15 & 265.96 & 0.9968 \\ BiLSTM & 50 & Leakyrelu & [PERSON] & 50 & 375.89 & 275.41 & 0.9966 \\ GRU & 50 & elu & [PERSON] & 50 & 373.14 & 275.81 & 0.9965 \\ \hline \end{tabular}
\end{table}
Table 7: Top 10 results of studies conducted with 50 epochs. The background of values with an R\({}^{2}\) value of 0.99 and above is painted green.  
Figure 15: Actual value-prediction graphs of the first 200 (**a**), and 30,000 (**b**) values according to the most successful model.  
The graph of the 10 most successful models according to Table 7 according to their activation function is shown in Figure 17. Accordingly, successful models can be listed as BiLSTM-Adam, LSTM-Adam, BiLSTM-Nadam, Simple RNN-Nadam, and GRU-Adam, respectively.  
The graph of the 10 most successful models according to Table 7, by model and optimizer, is shown in Figure 18. Accordingly, the most successful activation functions can be listed as ReLU, LeakyReLU, ELU, and tanh, respectively.  
Figure 16: True value line and prediction population for all values according to the most successful model. The blue line in the figure shows the true values, and the red dots show our predictions.  
Figure 17: Activation function–R\({}^{2}\) plots according to the top 10 results.  
In our study, we observed that performance varies according to the architectures and models employed. The same dataset produces different results depending on the method used. However, applying the same methods to different datasets will also produce different results. Indeed, studies have emphasized that data-driven approaches will play a significant role in estimation accuracy, and that activation function performance largely depends on the dataset [55; 56].