## 6 Conclusions  
The present work evaluated the accuracy of the hybrid GA-LSTM system and the deep learning neural networks BiLSTM and GRU to predict the unemployment rate in Ecuador. Three characteristics influencing the unemployment rate were considered and used to train the model. The proposed neural network models were based on three measures, MSE, MAE, and MAPE, to predict the unemployment rate. The predictions generated by the proposed models were calculated and compared with actual data; the GA-LSTM, BiLSTM, and GRU models demonstrated a high level of accuracy in capturing the various changes within the data set. However, because the differences are minor between the actual values and those predicted by the model, a paired \(t\)-test was required to make an informed decision. The results showed that the performance of the GA-LSTM models (one layer) vs. GRU (two layers) and BiLSTM (one layer) vs. BiLSTM (two layers) is very comparable. Likewise, the \(t\)-test allows testing whether the predictive accuracy of the prediction models differs significantly from the fundamental values of the unemployment rate. The hybrid GA-LSTM model (one layer) predicts results close to the actual unemployment rate scores. The predictions were calculated from the sample for 22 months based on the BiLSTM and GRU neural network models and 19 months in the case of the GA-LSTM hybrid model. In terms of economic policy, the results can help anticipate future trends in the level of unemployment in the country. Thus, those responsible for economic and labor policy can have advanced information on the behavior of the labor market in the coming months and develop support programs for the unemployed. Likewise, with accurate predictionsof the level of unemployment, governments can implement preventive measures against unemployment even before it occurs, either through the efficient allocation of public resources, incentives for the creation of new jobs, or the improvement of labor laws. That allows companies the possibility of increasing their labor supply. Regarding the relevance of these results, their importance lies in their usefulness as inputs for carrying out more in-depth research on the labor market in general and unemployment in particular, since it is necessary to consider what factors can influence unemployment predictions and thus propose more effective economic policies. Finally, these results can raise awareness in society about the moments in which the level of unemployment can increase, and thus families would be better informed to adjust their budgets in the face of an imminent rise in the level of unemployment. The importance of selecting an appropriate neural network architecture to evaluate the accuracy of unemployment prediction was fundamental, that is, deciding on the appropriate number of input nodes and hidden nodes, because it plays a vital role in evaluating the accuracy of neural network prediction. Different combinations of time window size and number of memory cell units can be tried to predict monthly unemployment performance. However, it requires a lot of time and experience on the part of researchers, so a genetic algorithm was adopted to optimize the hyperparameters of the hybrid model. Choosing a suitable neural network architecture is crucial when building unemployment rate prediction models, so one should be careful when choosing an unemployment rate prediction model. One of the main limitations of our study lies in the inherent complexity of the labor market in Ecuador since there have been abrupt changes in the unemployment environment. Moreover, there is no open data with detailed information on the local unemployment rate provided by official institutions. Therefore, we had to do a PDF transcript of the economic reports, including the pandemic period. It is important to note that while unemployment prediction is valuable in anticipating economic trends, policymakers must also understand the underlying causes of unemployment and develop strategies to address it effectively.