Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorLeite, Daniel Furtado-
Autor(es): dc.creatorŠkrjanc, Igor-
Data de aceite: dc.date.accessioned2026-02-09T12:30:18Z-
Data de disponibilização: dc.date.available2026-02-09T12:30:18Z-
Data de envio: dc.date.issued2020-06-26-
Data de envio: dc.date.issued2020-06-26-
Data de envio: dc.date.issued2019-11-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/41596-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0020025519306590#!-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1162612-
Descrição: dc.descriptionThis paper presents an online-learning ensemble framework for nonstationary time series prediction. Optimal granular fuzzy rule-based models with different objective functions and constraints are evolved from data streams. Evolving optimal granular systems (eOGS) consider multiobjective optimization, the specificity of information, model compactness, and variability and coverage of the data within the process of modeling data streams. Forecasts of individual base eOGS models are combined using averaging aggregation functions: ordered weighted averaging (OWA), weighted arithmetic mean, median, and linear non-inclusive centered OWA. Some aggregation functions use specific weights for the relevance of the base models and exclude extreme values and outliers. The weights of other aggregation functions are adapted over time based on a quadratic programming problem and the data within a sliding window. This paper investigates whether an online-learning ensemble can outperform individual eOGS models, and which aggregation function provides the most accurate forecasts. Real multivariate weather time series, particularly time series of daily mean temperature, air humidity, and wind speed from different weather stations, such as Paris–Orly, Frankfurt–Main, Reykjavik, and Oslo–Blindern, are used for evaluation. The results show that ensemble schemes outperform individual models. The proposed linear non-inclusive centered OWA function provided the most accurate numerical predictions.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier B.V.-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceInformation Sciences-
Palavras-chave: dc.subjectEvolving fuzzy systems-
Palavras-chave: dc.subjectEnsemble learning-
Palavras-chave: dc.subjectAggregation functions-
Palavras-chave: dc.subjectGranular computing-
Palavras-chave: dc.subjectWeather time series prediction-
Palavras-chave: dc.subjectSistema fuzzy-
Palavras-chave: dc.subjectFunções de agregação-
Palavras-chave: dc.subjectComputação granular-
Palavras-chave: dc.subjectPrevisão de séries temporais-
Palavras-chave: dc.subjectEstações meteorológicas-
Título: dc.titleEnsemble of evolving optimal granular experts, OWA aggregation, and time series prediction-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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