## 5 Discussion  
The above results show that the proposed GA-LSTM approach has performed well in predicting the unemployment rate in Ecuador. The approach's advantages compared to the individual models in the study are that GA allows for the automatic finding of the best parameters (i.e., optimal combination) for the LSTM algorithm. Finding the optimal values in machine learning (ML) is a great challenge because the hyperparameters in the algorithms control various aspects of the training of the data. This task in past research has primarily depended on the expertise of the researchers; however, it should be considered that time and computational limitations make it impossible to sweep a parameter space to find the optimal set of input variables for an algorithm [39]. Furthermore, satisfactory results can be obtained with a small number of features (three in this study) and a limited sample. The strengths and weaknesses of the models used in the study are shown below (Table 6). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & & **Strengths** & **Weaknesses** \\ \hline \multirow{6}{*}{BiLSTM} & \(\bullet\) & Ability to capture long-term dependencies of sequential data. & \(\bullet\) & The increased computational complexity of BiLSTM compared to LSTM and GRU results in increased training time, affecting the model’s ability to perform fast operations. \\  & \(\bullet\) & Battery relationship of input data is used to improve the learning capabilities of LSTM. & \(\bullet\) & The increased computational complexity of BiLSTM compared to LSTM and GRU results in increased training time, affecting the model’s ability to perform fast operations. \\  & \(\bullet\) & Fewer parameters and calculations than LSTM. & \(\bullet\) & Faster training because its architecture is more straightforward than LSTM. \\  & \(\bullet\) & In the field of unemployment rate prediction with a limited amount of data, GRU performs better than BiLSTM. & \(\bullet\) & In situations where computational efficiency and training times are critical, GRU networks may be preferable over BiLSTM. \\  & \(\bullet\) & To predict problems in time series analysis, it is recommended to use GRU instead of BiLSTM and select BiLSTM instead of LSTM. & \(\bullet\) & Furthermore, GRU is faster than the LSTM and BiLSTM algorithms [46]. \\  & \(\bullet\) & Lower computational complexity than LSTM and BiLSTM; they train faster and work better for small amounts of data [47]. & \(\bullet\) & The GA-LSTM model, when combining two techniques, is more complex and requires more computational resources and training time. \\  & \(\bullet\) & It can handle search space exploration better, leading to better model performance than more straightforward hyperparameter optimization methods, especially in complex or high-dimensionality problems. & \(\bullet\) & There is a need to adjust the specific parameters of the genetic algorithm and an increase in computational cost associated with evaluating multiple solutions in each generation. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the BiLSTM, GRU, and GA-LSTM models. Figure 4: Prediction results of different approaches. The comparison table provides a clear view of the strengths and weaknesses of the three recurrent neural network models, i.e., BiLSTM, GRU, and GA-LSTM. It is observed that each model has distinctive characteristics that make it suitable for different contexts and applications. BiLSTM excels at capturing long-term dependencies in sequential data, while GRU offers a simpler architecture and faster training times, making it preferable in situations where computational efficiency is critical. On the other hand, the hybrid GA-LSTM approach combines the search space exploration capability of genetic algorithms with the long-term dependency capture capability of LSTM, resulting in superior performance in terms of prediction accuracy. Unemployment rate compared to the other two models.