|
Registro Completo |
|
Biblioteca(s): |
Embrapa Pantanal. |
|
Data corrente: |
20/02/2020 |
|
Data da última atualização: |
20/02/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
BULHOES, J. S.; MARTINS, C. L.; OLIVEIRA, M. D. de; CALHEIROS, D. F.; CALIXTO, W. P. |
|
Afiliação: |
JUNIO S. BULHOES, IFMT; CRISTIANE L. MARTINS, IFMT; MARCIA DIVINA DE OLIVEIRA, CPAP; DEBORA FERNANDES CALHEIROS, SGE; WESLEY P. CALIXTO, IFG. |
|
Título: |
Indirect prediction system for variables that have gaps in their time series. |
|
Ano de publicação: |
2019 |
|
Fonte/Imprenta: |
Chaos, Solitons and Fractals, v. 131, 109509, p. 1-16, feb. 2019. |
|
DOI: |
https://doi.org/10.1016/j.chaos.2019.109509 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Gaps in time series as well as the absence of such series make the implementation of prediction system difficult. This paper proposes a new methodology to fill gaps in time series that do not present fixed sampling rate. This paper also proposes the development of two forecast models for time series. The first model is based on autoregressive multilayer neural network that uses only the desired time series, while the second one is developed with multilayer neural network that uses pattern recognition in order to perform indirect predictions of a certain variable. Therefore, the second model does not need the variable time series to make predictions, but any time series that has correlation with the desired variable. The methodology is tested in limnological variables collected in the Paraguay River since 1987, and the results observed in each process are presented in order to validate the methodology of gap filling and forecast used. |
|
Thesagro: |
Limnologia; Sistema de Informação. |
|
Thesaurus Nal: |
Limnology; Prediction; Time series analysis. |
|
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
|
Marc: |
LEADER 01689naa a2200241 a 4500
001 2120460
005 2020-02-20
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.chaos.2019.109509$2DOI
100 1 $aBULHOES, J. S.
245 $aIndirect prediction system for variables that have gaps in their time series.$h[electronic resource]
260 $c2019
520 $aGaps in time series as well as the absence of such series make the implementation of prediction system difficult. This paper proposes a new methodology to fill gaps in time series that do not present fixed sampling rate. This paper also proposes the development of two forecast models for time series. The first model is based on autoregressive multilayer neural network that uses only the desired time series, while the second one is developed with multilayer neural network that uses pattern recognition in order to perform indirect predictions of a certain variable. Therefore, the second model does not need the variable time series to make predictions, but any time series that has correlation with the desired variable. The methodology is tested in limnological variables collected in the Paraguay River since 1987, and the results observed in each process are presented in order to validate the methodology of gap filling and forecast used.
650 $aLimnology
650 $aPrediction
650 $aTime series analysis
650 $aLimnologia
650 $aSistema de Informação
700 1 $aMARTINS, C. L.
700 1 $aOLIVEIRA, M. D. de
700 1 $aCALHEIROS, D. F.
700 1 $aCALIXTO, W. P.
773 $tChaos, Solitons and Fractals$gv. 131, 109509, p. 1-16, feb. 2019.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pantanal (CPAP) |
|
