Forecasting oil commodity spot price in a data-rich environment

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the chang...

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Detalles Bibliográficos
Autores principales: Boubaker, Sabri, Liu, Zhenya, Zhang, Yifan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534472/
https://www.ncbi.nlm.nih.gov/pubmed/36217322
http://dx.doi.org/10.1007/s10479-022-05004-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534472/
https://www.ncbi.nlm.nih.gov/pubmed/36217322
http://dx.doi.org/10.1007/s10479-022-05004-8

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