J
João Fausto Lorenzato de Oliveira
Researcher at Universidade de Pernambuco
Publications - 40
Citations - 490
João Fausto Lorenzato de Oliveira is an academic researcher from Universidade de Pernambuco. The author has contributed to research in topics: Time series & Autoregressive integrated moving average. The author has an hindex of 7, co-authored 35 publications receiving 238 citations. Previous affiliations of João Fausto Lorenzato de Oliveira include Federal University of Pernambuco.
Papers
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Journal ArticleDOI
An intelligent hybridization of ARIMA with machine learning models for time series forecasting
Domingos S. de O. Santos Júnior,Domingos S. de O. Santos Júnior,João Fausto Lorenzato de Oliveira,Paulo S. G. de Mattos Neto +3 more
TL;DR: This work proposes a hybrid system that searches for a suitable function to combine the forecasts of linear and nonlinear models and attains superior performance when compared with single and hybrid models in the literature.
Book ChapterDOI
Primary familial brain calcifications.
TL;DR: The discovery of the genetic basis of PFBC provides not only a diagnostic tool, but also an insight into the pathomechanisms and potential therapeutic trials for this rare disease.
Journal ArticleDOI
A hybrid evolutionary decomposition system for time series forecasting
TL;DR: Experimental results show that the evolutionary hybrid system presented promising results in the forecasting domain using a hybrid evolutionary system composed by a simple exponential smoothing filter, ARIMA and autoregressive (AR) linear models and a SVR model.
Journal ArticleDOI
A Hybrid System Based on Dynamic Selection for Time Series Forecasting
TL;DR: In this paper, a modified dynamic selection (DS) algorithm is used to select the most suitable ML model to forecast a pattern of residual series and if it is a promising candidate to increase the accuracy of the time series forecast from the linear combination.
Journal ArticleDOI
Neural-Based Ensembles for Particulate Matter Forecasting
Paulo S. G. de Mattos Neto,Paulo Renato Alves Firmino,Hugo Siqueira,Yara de Souza Tadano,Thiago Antonini Alves,João Fausto Lorenzato de Oliveira,Manoel Henrique da Nóbrega Marinho,Francisco Madeiro +7 more
TL;DR: In this article, trainable and non-trainable combination methods are used for PM10 and PM2.5 time series forecasting for eight different locations, in Finland and Brazil, for different periods.