Improving the accuracy of intelligent forecasting models using the Perturbation Theory. - Eraylson G. Silva; Domingos S. de O. Junior; George D. C. Cavalcanti; Paulo S. G. de Mattos Neto (IJCNN, 2018)

An intelligent hybridization of ARIMA with machine learning models for time series forecasting. - Domingos S. de O. Junior; João F. L. de Oliveira; Paulo S. G. de Mattos Neto (Knowl. Based Syst., 2019)

A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting. - Paulo S. G. de Mattos Neto; João Fausto Lorenzato de Oliveira; Domingos S. de O. Junior; Hugo Valadares Siqueira; Manoel Henrique da Nobrega Marinho; Francisco Madeiro (IEEE Access, 2020)

An adaptive hybrid system using deep learning for wind speed forecasting. - Paulo S. G. de Mattos Neto; João F. L. de Oliveira; Domingos S. de O. Junior; Hugo Valadares Siqueira; Manoel Henrique da Nobrega Marinho; Francisco Madeiro (Inf. Sci., 2021)

A hybrid system based on ensemble learning to model residuals for time series forecasting. - Domingos S. de O. Junior; Paulo S. G. de Mattos Neto; João F. L. de Oliveira; George D. C. Cavalcanti (Inf. Sci., 2023)