M
Matheus Henrique Dal Molin Ribeiro
Researcher at Pontifícia Universidade Católica do Paraná
Publications - 34
Citations - 1236
Matheus Henrique Dal Molin Ribeiro is an academic researcher from Pontifícia Universidade Católica do Paraná. The author has contributed to research in topics: Ensemble learning & Mean squared error. The author has an hindex of 10, co-authored 30 publications receiving 578 citations. Previous affiliations of Matheus Henrique Dal Molin Ribeiro include Federal University of Technology - Paraná & Universidade Estadual de Maringá.
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Journal ArticleDOI
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.
Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Ramon Gomes da Silva,Viviana Cocco Mariani,Viviana Cocco Mariani,Leandro dos Santos Coelho,Leandro dos Santos Coelho +6 more
TL;DR: The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.
Journal ArticleDOI
Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Leandro dos Santos Coelho,Leandro dos Santos Coelho +3 more
TL;DR: The use of ensembles is recommended to forecast agricultural commodities prices one month ahead, since a more assertive performance is observed, which allows to increase the accuracy of the constructed model and reduce decision-making risk.
Journal ArticleDOI
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
Ramon Gomes da Silva,Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Viviana Cocco Mariani,Viviana Cocco Mariani,Leandro dos Santos Coelho,Leandro dos Santos Coelho +6 more
TL;DR: Bayesian regression neural network, cubist regression, k-nearest neighbors, quantile random forest, and support vector regression are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions, and hybridization of VMD outperformed single forecasting models regarding the accuracy.
Journal ArticleDOI
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Ramon Gomes da Silva,Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Sinvaldo Rodrigues Moreno,Viviana Cocco Mariani,Viviana Cocco Mariani,Leandro dos Santos Coelho,Leandro dos Santos Coelho +7 more
TL;DR: The decomposition-ensemble learning model is an efficient and accurate model for wind energy forecasting, and outperform the CEEMD, STACK, and single models in all forecasting horizons, with a performance improvement that ranges 0.06%–97.53%.
Journal ArticleDOI
Wavelet group method of data handling for fault prediction in electrical power insulators
Stéfano Frizzo Stefenon,Matheus Henrique Dal Molin Ribeiro,Matheus Henrique Dal Molin Ribeiro,Ademir Nied,Viviana Cocco Mariani,Viviana Cocco Mariani,Leandro dos Santos Coelho,Leandro dos Santos Coelho,Diovana Fátima Menegat da Rocha,Rafael Bartnik Grebogi,Rafael Bartnik Grebogi,António E. Ruano,António E. Ruano +12 more
TL;DR: A hybrid method that uses Wavelet Energy Coefficient (WEC) for feature extraction and Group Method of Data Handling (GMDH) for time series prediction is proposed, being defined as Wavelet GMDH.