Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model (2019)
- Authors:
- Autor USP: BATISTA, GUSTAVO ENRIQUE DE ALMEIDA PRADO ALVES - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.ins.2019.01.076
- Subjects: APRENDIZADO COMPUTACIONAL; ANÁLISE DE SÉRIES TEMPORAIS; MINERAÇÃO DE DADOS
- Keywords: Univariate analysis; Automatic parameter tuning; Multi-step-ahead prediction; Time series forecasting
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Information Sciences
- ISSN: 0020-0255
- Volume/Número/Paginação/Ano: v. 484, p. 302-337, May 2019
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
PARMEZAN, Antonio Rafael Sabino e SOUZA, Vinícius M. A e BATISTA, Gustavo Enrique de Almeida Prado Alves. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, v. 484, p. 302-337, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2019.01.076. Acesso em: 23 maio 2024. -
APA
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. de A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302-337. doi:10.1016/j.ins.2019.01.076 -
NLM
Parmezan ARS, Souza VMA, Batista GE de APA. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model [Internet]. Information Sciences. 2019 ; 484 302-337.[citado 2024 maio 23 ] Available from: https://doi.org/10.1016/j.ins.2019.01.076 -
Vancouver
Parmezan ARS, Souza VMA, Batista GE de APA. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model [Internet]. Information Sciences. 2019 ; 484 302-337.[citado 2024 maio 23 ] Available from: https://doi.org/10.1016/j.ins.2019.01.076 - Classifying and counting with recurrent contexts
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Informações sobre o DOI: 10.1016/j.ins.2019.01.076 (Fonte: oaDOI API)
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