Ensemble of evolving data clouds and fuzzy models for weather time series prediction

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorSoares, Eduardo-
Autor(es): dc.creatorCosta, Pyramo-
Autor(es): dc.creatorCosta, Bruno-
Autor(es): dc.creatorLeite, Daniel-
Data de aceite: dc.date.accessioned2026-02-09T12:40:39Z-
Data de disponibilização: dc.date.available2026-02-09T12:40:39Z-
Data de envio: dc.date.issued2019-07-22-
Data de envio: dc.date.issued2019-07-22-
Data de envio: dc.date.issued2018-03-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/35505-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S1568494617307573-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1166081-
Descrição: dc.descriptionThis paper describes a variation of data cloud-based intelligent method known as typicality-and-eccentricity-based method for data analysis (TEDA). The objective is to develop data-centric nonlinear and time-varying models to predict mean monthly temperature. TEDA is an incremental algorithm that considers the data density and scattering of clouds over the data space. The method does not require a priori knowledge of the dataset and user-defined parameters. However, if some knowledge about the number of clouds and rules is available, then it can be expressed through a single parameter. Past values of minimum, maximum and mean monthly temperature, as well as previous values of exogenous variables such as cloudiness, rainfall and humidity are considered in the analysis. A non-parametric Spearman correlation based method is proposed to rank and select the most relevant features and time delays for a more accurate prediction. The datasets were obtained from weather stations located in main Brazilian cities such as Sao Paulo, Manaus, Porto Alegre, and Natal. These cities are known to have particular weather characteristics. TEDA results are compared with results provided by the evolving Takagi–Sugeno (eTS) and the extended Takagi–Sugeno (xTS) methods. Additionally, an ensemble of cloud and fuzzy models and fuzzy aggregation operators is developed to give single-valued and granular predictions of the time series. Granular predictions convey a range of possible temperature values and give an idea about the error and uncertainty associated with the data.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceApplied Soft Computing-
Palavras-chave: dc.subjectEnsemble learning-
Palavras-chave: dc.subjectData clouds-
Palavras-chave: dc.subjectEvolving fuzzy systems-
Palavras-chave: dc.subjectWeather time series prediction-
Palavras-chave: dc.subjectOnline data stream-
Título: dc.titleEnsemble of evolving data clouds and fuzzy models for weather time series prediction-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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