Time series decomposition using automatic learning techniques for predictive models
This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series....
- Autores:
-
Silva, Jesús
H, H
Niebles Núñez, William
Ovallos-Gazabon, David
Varela, Noel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6237
- Acceso en línea:
- https://hdl.handle.net/11323/6237
https://repositorio.cuc.edu.co/
- Palabra clave:
- Automatic learning
Predictive models
Production prediction
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition. The agricultural sector will be used as the study subject. |
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