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2020 – today
- 2025
[j24]Renato Vimieiro
, Juliana Barcellos Mattos, Paulo S. G. de Mattos Neto:
EsmamDS: A more diverse exceptional survival model mining approach. Inf. Sci. 690: 121549 (2025)- 2024
- [j23]Ricardo de A. Araújo, Paulo S. G. de Mattos Neto, Nadia Nedjah, Sérgio C. B. Soares:
On the Sea Surface Temperature Forecasting Problem with Deep Dilation-Erosion-Linear Models. Big Data Res. 36: 100455 (2024) - [j22]Jair Paulino de Sales, Paulo S. G. de Mattos Neto, Paulo Renato A. Firmino:
A dynamic ensemble approach based on trend analysis to COVID-19 incidence forecast. Biomed. Signal Process. Control. 95: 106435 (2024) - [j21]Filipe Coelho de Lima Duarte, Paulo S. G. de Mattos Neto, Paulo Renato A. Firmino:
A hybrid recursive direct system for multi-step mortality rate forecasting. J. Supercomput. 80(13): 18430-18463 (2024) - [c25]Diogo M. Almeida, Paulo S. G. de Mattos Neto, Daniel Carvalho da Cunha:
A New Ensemble with Partition Size Variation Applied to Wind Speed Time Series. HAIS (2) 2024: 53-65 - [c24]Camila Ascendina, Paulo Salgado Gomes de Mattos Neto, João Fausto Lorenzato de Oliveira, Péricles B. C. de Miranda:
An Evolutionary Approach to Forecasting Models Hybridization. ICMLC 2024: 134-139 - 2023
- [j20]Cleber A. C. F. da Silva, Daniel Carneiro Rosa, Péricles B. C. de Miranda, Filipe R. Cordeiro, Tapas Si, André C. A. Nascimento, Rafael Ferreira Leite de Mello, Paulo S. G. de Mattos Neto:
A novel multi-objective grammar-based framework for the generation of Convolutional Neural Networks. Expert Syst. Appl. 212: 118670 (2023) - [j19]Domingos S. de O. Junior, Paulo S. G. de Mattos Neto, João F. L. de Oliveira, George D. C. Cavalcanti:
A hybrid system based on ensemble learning to model residuals for time series forecasting. Inf. Sci. 649: 119614 (2023) - [j18]Ricardo de A. Araújo, Paulo S. G. de Mattos Neto, Nadia Nedjah, Sérgio Castelo Branco Soares:
An error correction system for sea surface temperature prediction. Neural Comput. Appl. 35(16): 11681-11699 (2023) - [c23]Gabriel Mendes Matos, Paulo S. G. de Mattos Neto:
An Intelligent Dynamic Selection System Based on Nearest Temporal Windows for Time Series Forecasting. ICANN (6) 2023: 167-179 - [c22]Emilly Pereira Alves, Felipe Alberto B. S. Ferreira, Francisco Madeiro, Paulo S. G. de Mattos Neto, João Fausto Lorenzato de Oliveira:
Hybrid System with Dynamic Classification for Combining Time Series Forecasts. LA-CCI 2023: 1-6 - 2022
- [j17]Mouglas Eugênio Nasário Gomes, David Macêdo, Cleber Zanchettin, Paulo Salgado Gomes de Mattos Neto, Adriano L. I. Oliveira:
Multi-human Fall Detection and Localization in Videos. Comput. Vis. Image Underst. 220: 103442 (2022) - [j16]João F. L. de Oliveira, Eraylson G. Silva, Paulo S. G. de Mattos Neto:
A Hybrid System Based on Dynamic Selection for Time Series Forecasting. IEEE Trans. Neural Networks Learn. Syst. 33(8): 3251-3263 (2022) - [c21]Felipe N. A. Pereira, Mateus F. B. Soares, Conceição Rocha, Tales T. Alves, Tiago H. R. P. Gonçalves, José R. da Silva, Tsang Ing Ren, Paulo S. G. de Mattos Neto, Edna Natividade da Silva Barros:
A Library and Web Platform for RoboCup Soccer Matches Data Analysis. RoboCup 2022: 177-189 - [c20]Mateus F. B. Soares, Tsang Ing Ren, Paulo S. G. de Mattos Neto, Edna Natividade da Silva Barros:
Web Soccer Monitor: An Open-Source 2D Soccer Simulation Monitor for the Web and the Foundation for a New Ecosystem. RoboCup 2022: 190-199 - 2021
- [j15]Paulo S. G. de Mattos Neto, Paulo Renato A. Firmino, Hugo Siqueira, Yara de Souza Tadano, Thiago Antonini Alves, João Fausto Lorenzato de Oliveira, Manoel Henrique da Nobrega Marinho, Francisco Madeiro:
Neural-Based Ensembles for Particulate Matter Forecasting. IEEE Access 9: 14470-14490 (2021) - [j14]Eraylson G. Silva, Paulo S. G. de Mattos Neto, George D. C. Cavalcanti:
A Dynamic Predictor Selection Method Based on Recent Temporal Windows for Time Series Forecasting. IEEE Access 9: 108466-108479 (2021) - [j13]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:
An adaptive hybrid system using deep learning for wind speed forecasting. Inf. Sci. 581: 495-514 (2021) - [j12]Paulo S. G. de Mattos Neto, João F. L. de Oliveira, Priscilla Bassetto, Hugo Valadares Siqueira, Luciano Barbosa, Emilly Pereira Alves, Manoel Henrique da Nobrega Marinho, Guilherme Ferretti Rissi, Fu Li:
Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble. Sensors 21(23): 8096 (2021) - [c19]Cleber A. C. F. da Silva, Daniel Carneiro Rosa, Péricles B. C. de Miranda, Filipe R. Cordeiro, Tapas Si, André C. A. Nascimento, Rafael Ferreira Leite de Mello, Paulo S. G. de Mattos Neto:
A Multi-Objective Grammatical Evolution Framework to Generate Convolutional Neural Network Architectures. CEC 2021: 2187-2194 - [c18]Tathiana M. Barchi, Lucas Francesco Piccioni Costa, Erickson Diogo Pereira Puchta, Marcella S. R. Martins, Mauren Louise Sguario C. Andrade, Paulo S. G. de Mattos Neto, Hugo Valadares Siqueira:
A Hybrid Model With Error Correction for Wind Speed Forecasting. LA-CCI 2021: 1-6 - [i2]Juliana Barcellos Mattos, Paulo S. G. de Mattos Neto, Renato Vimieiro:
EsmamDS: A more diverse exceptional survival model mining approach. CoRR abs/2109.02610 (2021) - 2020
- [j11]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:
A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting. IEEE Access 8: 191365-191377 (2020) - [j10]João Fausto Lorenzato de Oliveira, Luciano Demetrio Santos Pacífico, Paulo Salgado Gomes de Mattos Neto, Emanoel Francisco Spósito Barreiros, Cleyton Mário de Oliveira Rodrigues, Adauto Trigueiro de Almeida Filho:
A hybrid optimized error correction system for time series forecasting. Appl. Soft Comput. 87: 105970 (2020) - [j9]Paulo S. G. de Mattos Neto, George D. C. Cavalcanti, Paulo Renato A. Firmino, Eraylson G. Silva, Sérgio R. P. Vila Nova Filho:
A temporal-window framework for modelling and forecasting time series. Knowl. Based Syst. 193: 105476 (2020) - [c17]Juliana Barcellos Mattos, Eraylson G. Silva, Paulo S. G. de Mattos Neto, Renato Vimieiro:
Exceptional Survival Model Mining. BRACIS (2) 2020: 307-321 - [c16]Paulo S. G. de Mattos Neto, Péricles B. C. de Miranda, George D. C. Cavalcanti, Tapas Si, Filipe R. Cordeiro, Mayara Castro:
Layers Sequence Optimizing for Deep Neural Networks using Multiples Objectives. CEC 2020: 1-8 - [c15]Eraylson G. Silva, George D. C. Cavalcanti, João Fausto Lorenzato de Oliveira, Paulo S. G. de Mattos Neto:
On the evaluation of dynamic selection parameters for time series forecasting. IJCNN 2020: 1-7 - [i1]Hugo Abreu Mendes, Henrique Ferreira Nunes, Paulo Salgado Gomes de Mattos Neto:
Data integration and prediction models of photovoltaic production from Brazilian northeastern. CoRR abs/2001.10866 (2020)
2010 – 2019
- 2019
- [j8]Domingos S. de O. Junior, João F. L. de Oliveira, Paulo S. G. de Mattos Neto:
An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl. Based Syst. 175: 72-86 (2019) - [c14]Eraylson Galdino da Silva, Paulo Salgado Gomes de Mattos Neto, João Fausto Lorenzato de Oliveira:
Hybrid System for Time Series using Iterative Residual Forecasting Models. BRACIS 2019: 872-877 - [c13]Robson D. A. Timoteo, Daniel C. Cunha, Paulo S. G. de Mattos Neto:
A Novel Combining-Based Method of Pool Generation for Ensemble Regression Problems. FLAIRS 2019: 80-85 - 2018
- [c12]Diogo M. Almeida, Paulo S. G. de Mattos Neto, Daniel C. Cunha:
Hybrid Time Series Forecasting Models Applied to Automotive On-Board Diagnostics Systems. IJCNN 2018: 1-8 - [c11]Eraylson G. Silva, Domingos S. de O. Junior, George D. C. Cavalcanti, Paulo S. G. de Mattos Neto:
Improving the accuracy of intelligent forecasting models using the Perturbation Theory. IJCNN 2018: 1-7 - 2017
- [j7]Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira, Aranildo R. Lima, Germano C. Vasconcelos, George D. C. Cavalcanti:
A perturbative approach for enhancing the performance of time series forecasting. Neural Networks 88: 114-124 (2017) - [j6]Paulo S. G. de Mattos Neto, George D. C. Cavalcanti, Francisco Madeiro:
Nonlinear combination method of forecasters applied to PM time series. Pattern Recognit. Lett. 95: 65-72 (2017) - 2016
- [j5]Edson Mata, Silvio Bandeira, Paulo S. G. de Mattos Neto, Waslon Terllizzie A. Lopes, Francisco Madeiro:
Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design. Sensors 16(11): 1963 (2016) - [c10]Hector N. B. Pinheiro, Sergio R. F. Vieira, Tsang Ing Ren, George D. C. Cavalcanti, Paulo S. G. de Mattos Neto:
Type-2 fuzzy GMM for text-independent speaker verification under unseen noise conditions. ICASSP 2016: 5490-5494 - [c9]Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting. IJCNN 2016: 2104-2109 - 2015
- [j4]Paulo Renato A. Firmino, Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
Error modeling approach to improve time series forecasters. Neurocomputing 153: 242-254 (2015) - 2014
- [j3]Paulo S. G. de Mattos Neto, Francisco Madeiro, Tiago A. E. Ferreira, George D. C. Cavalcanti:
Hybrid intelligent system for air quality forecasting using phase adjustment. Eng. Appl. Artif. Intell. 32: 185-191 (2014) - [j2]David Augusto Silva, Gabriela I. L. Alves, Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
Measurement of Fitness Function efficiency using Data Envelopment Analysis. Expert Syst. Appl. 41(16): 7147-7160 (2014) - [j1]Paulo Renato A. Firmino, Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
Correcting and combining time series forecasters. Neural Networks 50: 1-11 (2014) - 2012
- [b1]Paulo Salgado Gomes de Mattos Neto:
Um Método para análise de mercados de ações utilizando séries temporais de índices financeiros. Federal University of Pernambuco, Recife, Brazil, 2012 - [c8]Henrique Correia Torres Santos, Gabriela I. L. Alves, Neilson F. de Lima, Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
A System Based on Swarm Particle Optimization to Extract Knowledge from Times Series Data. SBRN 2012: 244-249 - 2011
- [c7]Gustavo H. T. Ribeiro, Paulo S. G. de Mattos Neto, George D. C. Cavalcanti, Ing Ren Tsang:
Lag selection for time series forecasting using Particle Swarm Optimization. IJCNN 2011: 2437-2444 - [c6]Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira, George D. C. Cavalcanti:
A simulation environment for volatility analysis of developed and in development markets. IJCNN 2011: 2450-2456 - 2010
- [c5]Aranildo Rodrigues Lima Junior, David Augusto Silva, Paulo Salgado Gomes de Mattos Neto, Tiago A. E. Ferreira:
An experimental study of fitness function and time series forecasting using artificial neural networks. GECCO (Companion) 2010: 2015-2018 - [c4]Paulo S. G. de Mattos Neto, Aranildo R. Lima, Tiago A. E. Ferreira, George D. C. Cavalcanti:
An intelligent perturbative approach for the time series forecasting problem. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [c3]Paulo S. G. de Mattos Neto, Aranildo Rodrigues Lima Junior, Tiago A. E. Ferreira:
Time series forecasting using a perturbative intelligent system. GECCO 2009: 1477-1478 - [c2]Paulo S. G. de Mattos Neto, Gustavo G. Petry, Aranildo Rodrigues Lima Junior, Tiago A. E. Ferreira:
Combining Artificial Neural Network and Particle Swarm System for time series forecasting. IJCNN 2009: 2230-2237 - [c1]L. J. Aranildo Rodrigues, Paulo S. G. de Mattos Neto, Tiago A. E. Ferreira:
A prime step in the time series forecasting with hybrid methods: The fitness function choice. IJCNN 2009: 2703-2710
Coauthor Index
aka: Tiago A. E. Ferreira
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