## 2 Related Works
The study shows that the type of fuzzy relationship affects the forecast performance and that the proposed method can provide several forecast intervals with different degrees of confidence. [PERSON]. [18] determined the best model to forecast the Spanish unemployment rate. Several models are proposed: barycentric, VAR, and, as proposed, linear regression and neural networks using the seasonally adjusted monthly unemployment rates provided by EUROSTAT for 11 European countries, including Spain, covering the period from January 1987 to October 2011. In the experimental setup, the proposed reconstruction function and neural network are adjusted, calculating the normalized mean square error (NMSE) for embedding dimensions from 2 to 10 and a different number of neighbors (1 to 150) in the first case and 2 to 10 nodes in the second. The parameters are selected individually in the training phase for each country. The study concludes that the linear regression predictor can improve linear techniques for forecasting in certain countries, especially in long-term forecasting. Furthermore, it is observed that the results provided by the VAR model and the barycentric predictor worsen as the time horizon increases. At the same time, linear regression and neural networks show better results, especially in long-term forecasts. [PERSON] et al. [2] determined the best model to forecast unemployment in several countries, including Canada, Germany, Japan, the Netherlands, New Zealand, Sweden, and Switzerland. Several models were used for prediction, including autoregressive integrated moving average (ARIMA), ANN, autoregressive neural network (ARN), SVM,ARIMA with support vector machine (ARIMA-SVM), ARIMA with artificial neural network (ARIMA-ANN), and ARIMA with autoregressive neural network (ARIMA-ARNN), with monthly and quarterly data of seasonally adjusted unemployment rates for the mentioned countries, obtained from open access data repositories such as FRED Economic Data sets and the OECD data repository. The ARIMA model is fitted to the data, and the ARNN model is then trained on the ARIMA residuals to capture the residual nonlinearities for each country. Then, the ARIMA forecast results and the residual ARNN forecasts are summed to obtain the final forecast values. The results are compared with other models, and performance metrics such as RMSE, MAE, and MAPE are used to evaluate the accuracy of the predictions. The results indicate that the proposed hybrid ARIMA-ARNN model combines the ability of ARIMA models to capture linearity with the ability of ARNN models to capture nonlinearities in the ARIMA residuals, resulting in better forecast accuracy in comparison with other individual and hybrid models. [PERSON] et al. [4] determined the best model to forecast the US unemployment rate. The study proposes a combined multi-granularity model based on fuzzy trend forecasting, automatic clustering, and particle swarm optimization (PSO) techniques, using the monthly civil unemployment rate from 1 January 1948 to 1 December 2013, as a data source, which includes the unemployment rate, the average duration of unemployment, and the employment-to-population ratio. In the experimental setup, three experiments are performed using different data sets and comparison methods. Automatic clustering algorithms generate different interval lengths, and then fuzzy time series techniques and granular computing theory are applied to forecast fuzzy trends. The results indicate that the proposed model significantly improves the forecasting accuracy of the civil unemployment rate in the USA. Likewise, [PERSON] and [PERSON]. [19] determined the best model to forecast the US unemployment rate. The proposed hybrid model is a stochastic linear autoregressive moving average with an exogenous variable and GARCHX (ARMAX-GARCHX) using the bivariate time series data set, including US unemployment and exchange rates.