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Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble.
Sensors (Basel). 2021 Dec 3;21(23):8096. doi: 10.3390/s21238096.
Sensors (Basel). 2021.
PMID: 34884100
Free PMC article.
A Hybrid System Based on Dynamic Selection for Time Series Forecasting.
de Oliveira JFL, Silva EG, de Mattos Neto PSG.
de Oliveira JFL, et al. Among authors: de mattos neto psg.
IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3251-3263. doi: 10.1109/TNNLS.2021.3051384. Epub 2022 Aug 3.
IEEE Trans Neural Netw Learn Syst. 2022.
PMID: 33513115
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A perturbative approach for enhancing the performance of time series forecasting.
de Mattos Neto PS, Ferreira TA, Lima AR, Vasconcelos GC, Cavalcanti GD.
de Mattos Neto PS, et al.
Neural Netw. 2017 Apr;88:114-124. doi: 10.1016/j.neunet.2017.02.004. Epub 2017 Feb 10.
Neural Netw. 2017.
PMID: 28236678
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An Approach to Improve the Performance of PM Forecasters.
de Mattos Neto PS, Cavalcanti GD, Madeiro F, Ferreira TA.
de Mattos Neto PS, et al.
PLoS One. 2015 Sep 28;10(9):e0138507. doi: 10.1371/journal.pone.0138507. eCollection 2015.
PLoS One. 2015.
PMID: 26414182
Free PMC article.
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Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design.
Mata E, Bandeira S, de Mattos Neto P, Lopes W, Madeiro F.
Mata E, et al.
Sensors (Basel). 2016 Nov 23;16(11):1963. doi: 10.3390/s16111963.
Sensors (Basel). 2016.
PMID: 27886061
Free PMC article.
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Hybrid systems using residual modeling for sea surface temperature forecasting.
de Mattos Neto PSG, Cavalcanti GDC, de O Santos Júnior DS, Silva EG.
de Mattos Neto PSG, et al.
Sci Rep. 2022 Jan 11;12(1):487. doi: 10.1038/s41598-021-04238-z.
Sci Rep. 2022.
PMID: 35017537
Free PMC article.
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Correcting and combining time series forecasters.
Firmino PR, de Mattos Neto PS, Ferreira TA.
Firmino PR, et al.
Neural Netw. 2014 Feb;50:1-11. doi: 10.1016/j.neunet.2013.10.008. Epub 2013 Nov 1.
Neural Netw. 2014.
PMID: 24239986
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