## 2 Related Works  
This section will review related works on models applied to unemployment rate prediction. Macroeconomic data is obtained from various sources, such as government databases, economic firms, banks, and social networks; the latter is used in novel methods for nowcasting economic forecasts and more timely forecasting of economic indicators. For example, [PERSON] et al. [6] demonstrated how unemployment and employment statistics are reflected in daily Twitter activity. [PERSON]. [7] used social media data to predict unemployment in South Korea, combining sentiment analysis and grammar taggingwith ARIMA, ARIMAX, and ARX models. Furthermore, [PERSON] et al., [PERSON], and [PERSON]. [8, 9] showed how the Google Trends index improves unemployment forecasts in Spain and the Visegrad Group, respectively. [PERSON] and [PERSON]. [10] found that the Google index (GI) improves quarterly unemployment predictions in the US. [PERSON] et al. [11] developed a data mining framework using neural networks (NN) and support vector regressions (SVR) to forecast US unemployment, outperforming traditional forecasting approaches. Other studies have highlighted the potential of search engine queries to predict economic indicators [12, 13, 14]. Over time, several models have been developed to predict the unemployment rate, including traditional statistical and econometric models, intelligent computational models, and hybrid models. [PERSON] et al. [3] determined the best model to forecast the Romanian unemployment rate. The monthly unemployment rate was used from January 2000 to December 2020. These data were obtained from Eurostat, specifically from the European Union Labor Force Survey (EU-LPS). Several models were considered for forecasting, including seasonal autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt-Winters, ETS (error, trend, seasonal), and neural network autoregression (NNAR). The experimental configuration for the Holt-Winters multiplicative model was smoothing parameters: Alpha (level) = 0.6928, Beta (trend) = 0.0001, Gamma (seasonal) = 0.0001, AIC = 630.187, AICc = 633.278, and BIC = 687.566. For the NNAR model, the configuration NNAR(1,1,k)\({}_{12}\) was used, where k varies from 1 to 14, and for the SARIMA model, the configuration SARIMA(0, 1, 6)(1, 0, 1)\({}_{12}\) was used. According to the root mean squared error (RMSE) and mean absolute error (MAE) values, the NNAR model showed better forecast performance. However, based on the mean absolute percentage error (MAPE) metric, the SARIMA model demonstrated higher forecast accuracy. Furthermore, the Diebold-Mariano test revealed differences in forecast performance between SARIMA and NNAR, concluding that the NNAR model is the best for modeling and forecasting the unemployment rate. On the other hand, [PERSON] and [PERSON]. [15] focused on forecasting the unemployment rate in Germany using the data set of unadjusted monthly unemployment rates of the 16 federal states of Germany, as well as the aggregate series for West and East Germany, during the sample period from January 1991 to February 2013. The Bayesian model averaging with periodic autoregressive (BMA-PAR) was proposed, together with seasonal autoregressive moving average (SARMA), seasonal MEANS (PMEANS), and Bayesian PAR (BPAR). In the experimental configuration, five simulation experiments were carried out to generate trajectories and evaluate different model specifications for stochastic processes such as PAR(1), SAR(1), and SARMA(1, 0) \(\times\) (1, 1), among others, with specific parameters for each simulation design, and the BMA-PAR model was compared with the BPAR(1) and SARMA(0, 11) \(\times\) (1, 0)\({}_{12}\) models. PAR models were found to have advantages in cases of periodic unit roots. Furthermore, the results support using a model combination (BMA-PAR) to improve predictive accuracy compared to a single model (BPAR). [PERSON].