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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Bravo, J. M. & Afshin Ashofteh (2023). Ensemble Methods for Consumer Price Inflation Forecasting. In Atas da 23ª Conferência da Associação Portuguesa de Sistemas de Informação.: Associação Portuguesa de Sistemas de Informação, APSI.
Exportar Referência (IEEE) 
J. M. Bravo and A. Ashofteh,  "Ensemble Methods for Consumer Price Inflation Forecasting", in Atas da 23ª Conferência da Associação Portuguesa de Sistemas de Informação, Associação Portuguesa de Sistemas de Informação, APSI, 2023
Exportar BibTeX 
@inproceedings{bravo2023_1783946233397,
	author = "Bravo, J. M. and Afshin Ashofteh",
	title = "Ensemble Methods for Consumer Price Inflation Forecasting",
	booktitle = "Atas da 23ª Conferência da Associação Portuguesa de Sistemas de Informação",
	year = "2023",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.18803/capsi.v23.317-336",
	publisher = "Associação Portuguesa de Sistemas de Informação, APSI",
	address = "",
	organization = "",
	url = "https://aisel.aisnet.org/capsi2023/25/"
}
Exportar RIS 
TY  - CPAPER
TI  - Ensemble Methods for Consumer Price Inflation Forecasting
T2  - Atas da 23ª Conferência da Associação Portuguesa de Sistemas de Informação
AU  - Bravo, J. M.
AU  - Afshin Ashofteh
PY  - 2023
DO  - 10.18803/capsi.v23.317-336
UR  - https://aisel.aisnet.org/capsi2023/25/
AB  - Inflation forecasting is one of the central issues in micro and macroeconomics. Standard forecasting methods tend to follow a "winner-take-all" approach by which, for each time series, a single believed to be the best method is chosen from a pool of competing models. This paper investigates the predictive accuracy of a metalearning strategy called Arbitrated Dynamic Ensemble (ADE) in inflation forecasting using United States data. The findings show that: i) the SARIMA model exhibits the best average rank relative to ADE and competing state-of-the-art model combination and metalearning methods; ii) the ADE methodology presents a better average rank compared to widely used model combination approaches, including the original Arbitrating approach, Stacking, Simple averaging, Fixed Share, or weighted adaptive combination of experts; iii) the ADE approach benefits from combining the base-learners as opposed to selecting the best forecasting model or using all experts; iv) the method is sensitive to the aggregation (weighting) mechanism.
ER  -