Models for optimising the theta method and their relationship to state space models (2016)
- Authors:
- Autor USP: LOUZADA NETO, FRANCISCO - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.ijforecast.2016.02.005
- Subjects: ESTATÍSTICA APLICADA; INFERÊNCIA BAYESIANA; INFERÊNCIA ESTATÍSTICA; REGRESSÃO LINEAR
- Keywords: Time series forecasting; Theta method; State Space Models; M3-Competition
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: International Journal of Forecasting
- ISSN: 0169-2070
- Volume/Número/Paginação/Ano: v. 32, n. 4, p. 1151-1161, Out.-Dez. 2016
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by
-
ABNT
FIORUCCI, José A et al. Models for optimising the theta method and their relationship to state space models. International Journal of Forecasting, v. 32, n. 4, p. 1151-1161, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ijforecast.2016.02.005. Acesso em: 28 mar. 2024. -
APA
Fiorucci, J. A., Pellegrini, T. R., Louzada, F., Petropoulos, F., & Koehler, A. B. (2016). Models for optimising the theta method and their relationship to state space models. International Journal of Forecasting, 32( 4), 1151-1161. doi:10.1016/j.ijforecast.2016.02.005 -
NLM
Fiorucci JA, Pellegrini TR, Louzada F, Petropoulos F, Koehler AB. Models for optimising the theta method and their relationship to state space models [Internet]. International Journal of Forecasting. 2016 ; 32( 4): 1151-1161.[citado 2024 mar. 28 ] Available from: https://doi.org/10.1016/j.ijforecast.2016.02.005 -
Vancouver
Fiorucci JA, Pellegrini TR, Louzada F, Petropoulos F, Koehler AB. Models for optimising the theta method and their relationship to state space models [Internet]. International Journal of Forecasting. 2016 ; 32( 4): 1151-1161.[citado 2024 mar. 28 ] Available from: https://doi.org/10.1016/j.ijforecast.2016.02.005 - Cooperative parallel particle filters for online model selection and applications to urban mobility
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Informações sobre o DOI: 10.1016/j.ijforecast.2016.02.005 (Fonte: oaDOI API)
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