## 2 Related Works
[16] addressed the problem of determining the best model for forecasting the unemployment rate in Greater Poland. As a data set, monthly panel data were used for 35 LAU1s of Greater Poland over 123 months (January 2005 to March 2015), with an evaluation period of 13 months (April 2015 to April 2016). Notably, a monthly frequency of time series was used instead of the quarterly or annual frequency commonly used. The vector autoregressive (VAR), spatial VAR (VARS), neural network (NN), and neural network seasonal (NNS) models were used, and, as proposed, the spatial vector autoregressions (SpVAR) and spatial neural network (SpNN) models were used. Spatial models were compared with their non-spatial and seasonal equivalents, highlighting that including a spatial component in the models significantly improves the accuracy of the forecasts. The overall performance of SpVAR was 30% better than that of spatial artificial neural networks (SpANN). [PERSON]. [5] determined the best models to forecast the unemployment rate in several countries from different regions (Mediterranean, Baltic, Balkan, Nordic, and Benelux) for different forecast horizons. Monthly data on seasonally adjusted unemployment rates were used for 22 countries in the regions above. The period covered was from January 2000 to December 2014, considering predictions of one step, three steps, and twelve steps forward for the following 12 months until December 2017. The fractional autoregressive integrated moving average (FARIMA), FARIMA with generalized autoregressive conditional heteroskedasticity (FARIMA/GARCH), artificial neural network (ANN) models, support vector machines (SVMs), and multivariate adaptive regression splines (MARS) were proposed. The models were configured as follows: (a) in the autoregressive moving average (ARMA) and FARIMA model, the order of the model (p, q) was first determined using the Bayesian information criterion (BIC) to select the best combination, then the parameters were estimated for the FARIMA model; (b) in the FARIMA/GARCH model, a procedure similar to the FARIMA model was followed, but the model fit and a GARCH(1, 1) model were considered to capture the conditional variance of the errors; (c) In the ANN model, the resampling rate (k = 1), the number of input variables (1-4 lagged variables), the number of nodes in the hidden layer (1-10 nodes), the training epochs (500 periods), and the sigmoid activation function were determined; (d) in the SVM model, a high-dimensional representation of the input space and the model with the lowest RMSE were used; and (e) in the MARS model, a recursive partitioning procedure was used to build the model using two stages: the forward and backward step. It was found that there is no single globally accepted model and that both forecast horizon and geographic location must be considered to select an appropriate approach. FARIMA models were the preferable option for one-step-ahead forecasts; while neural network approaches achieved comparable results for more extended periods (h = 12), neural network approaches achieved comparable results with FARIMA-based models. It was observed that for a three-step forecast horizon (h = 3), the Holt-Winters model was more suitable, and it is suggested that the selection of the most suitable forecast approaches for different forecast horizons needs further investigation. [PERSON] et al. [17] focused on forecasting Malaysia's unemployment rate. To make the forecast, Malaysia's unemployment rate is used from 1982 to 2013. The use of fuzzy time series (FTS) is proposed with a natural partitioning approach considering introducing a fuzzy time series model using data in the form of trapezoidal fuzzy numbers and a natural partitioning length approach, using two types of fuzzy relations: first and second order, and that the proposed model can produce predicted values under different degrees of confidence.