## 5 Results of the Study
### Results of the Estimation Made Using Four Different Models and 11 Different Optimization Methods  
Figures 5 and 6 are plotted for the results of the seven most successful optimization methods according to the four models. In this study, ReLU was used as the activation function, and the study was conducted over 50 epochs. According to Figure 5, when the most successful results are looked at, Adam and Nadam come first, followed by Adamax and RMSprop. The Adagrad and SGD methods are not shown in Figure 5 because of their lower performance. In Figure 6, the Adagrad, SGD, and SGDTrue methods are not shown. According to Figure 6, BiLSTM and LSTM stand out as the most successful models, although there is no clear distinction. This conclusion is supported by similar studies conducted with different datasets, which reached R\({}^{2}\) values of 0.99 and MAE values of 0.092 [50; 51]. Figure 8: GRU: seven optimizers–10–50–100 epochs–R\({}^{2}\) graph. Figure 6: Four models–five optimizers–R\({}^{2}\) graph. Figure 7: LSTM: six optimizers–10–50–100 epochs–R\({}^{2}\) graph. In Figure 7, for the RMSprop\({}^{\star}\) method, the epoch value of 100 was removed from the graph because it was too low. The R\({}^{2}\) value increases as the number of epochs increases, while for both RMSprop and RMSprop\({}^{\star}\), the value increases at 50 epochs but decreases at 100 epochs. In Figure 8, as the number of epochs increases, the R\({}^{2}\) value increases, while for RMSprop this value increases at 50 epochs and decreases at 100 epochs. In Figure 9, as the number of epochs increases, the R\({}^{2}\) value increases, while for both RMSprop\({}^{\star}\) and Nadam, the value increases at 50 epochs but decreases at 100 epochs. Despite this, the Nadam method stands out as the most successful model. In Figure 10, as the number of epochs increases, the R\({}^{2}\) value increases, while for Adam, the value increases at 50 epochs but decreases at 100 epochs. In addition, the 70 epoch value could not be measured in the RMSprop\({}^{\star}\) method because it gave a NaN error due to the exploding gradients problem. Figure 10: BiLSTM: six optimizers–10–50–70 epochs–R\({}^{2}\) graph. Figure 9: SimpleRNN: seven optimizers–10–50–100 epochs–R\({}^{2}\) graph. Results of the Study Using Four Different Models, Six Different Activation Functions, and 11 Different Optimizers  
In Figures 11-14 below, the results of the experiments with four different architectures (LSTM, GRU, SimpleRNN, and BiLSTM), six different activation functions (ReLU, tanh, ELU, LeakyReLU, sigmoid, softmax), and six different optimizers are plotted. The epoch value is taken as 50. The Adagrad method is not shown in the graph because it obtained negative values in 8 out of 24 trials. The SGD method is not shown in the graph due to its low performance. Additionally, negative values in other methods are not shown. As can be seen from Figures 11a, 12a, 13a and 14a, the most successful activation functions are ReLU, tanh, ELU, and LeakyReLU. It can be seen that sigmoid and softmax are less successful. In Figure 13a,b, the study using the SimpleRNN-LeakyReLU-RMSprop* methods was deleted because its result value was low. Moreover, the SimpleRNN-ELU-RMSprop* model in Figure 13a,b and the BiLSTM-ELU-RMSprop* model in Figure 14a,b suffer from the explo  
Figure 11: (**a**) Six optimizers–R2 plots of six activation functions with LSTM, (**b**) LSTM–six activation functions–R2 plots of six optimizers. Figure 12: (**a**) Six optimizers–R2 plots of six activation functions with GRU, (**b**) GRU–six activation functions–R2 plots of six optimizers. As can be seen from Figures 11b, 12b, 13b and 14b, the most successful optimizers are Adam, Nadam, Adamax, and RMSprop. It can be seen that RMSprop* and SGDtrue are less successful. It can be seen that the SGDtrue method is more successful than the SGD method in all of the studies except one trial. When compared with these two methods, the SGDfalse method is seen to be quite unsuccessful.