RESUMO

ABSTRACT This study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups.

RESUMO Este trabalho apresenta uma previsão de classificação em grupos para as equipes do campeonato brasileiro de futebol tanto da série A quanto da série B a partir dos resultados do primeiro turno de cada campeonato. Com previsões assertivas do grupo onde um time irá finalizar o campeonato, pode-se realizar um planejamento estratégico no elenco tal como novas contratações, treinos específicos dos atletas e possíveis campeonatos que o time terá direito de participar de acordo com o grupo em que se classificar. Para encontrar as previsões, aplicou-se as técnicas rede neural artificial Multi Layer Perceptron (MLP) e Support Vector Machine (SVM). Resultados preliminares indicam que a metodologia proposta é bastante promissora, acertando em mais de 40% dos casos com a MLP e quase 50% com o SVM. Além disso, os resultados indicam que a metodologia também é capaz de realizar uma boa previsão errando em um grupo do verdadeiro grupo ao final do campeonato. A técnica SVM se mostrou um pouco superior à MLP. Um pós processamento nos resultados do SVM é aplicado aos dados do ano de 2018 da série A do campeonato brasileiro, resultando em 85% de acertos dos grupos.

RESUMO

Prediction of financial time series is a great challenge for statistical models. In general, the stock market times series present high volatility due to its sensitivity to economic and political factors. Furthermore, recently, the covid-19 pandemic has caused a drastic change in the stock exchange times series. In this challenging context, several computational techniques have been proposed to improve the performance of predicting such times series. The main goal of this article is to compare the prediction performance of five neural network architectures in predicting the six most traded stocks of the official Brazilian stock exchange B3 from March 2019 to April 2020. We trained the models to predict the closing price of the next day using as inputs its own previous values. We compared the predictive performance of multiple linear regression, Elman, Jordan, radial basis function, and multilayer perceptron architectures based on the root of the mean square error. We trained all models using the training set while hyper-parameters such as the number of input variables and hidden layers were selected using the testing set. Moreover, we used the trimmed average of 100 bootstrap samples as our prediction. Thus, our approach allows us to measure the uncertainty associate with the predicted values. The results showed that for all times series, considered all architectures, except the radial basis function, the networks tunning provide suitable fit, reasonable predictions, and confidence intervals.