Reconstruction of meteorological records with PCA-based analog ensemble methods

Breve, Murilo M.; Balsa, Carlos; Rufino, José

http://hdl.handle.net/10198/36124

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Autores

Breve, Murilo M.

Balsa, Carlos

Rufino, José

Resumo(s)

The Analog Ensemble (AnEn) method has been used to reconstruct missing data in time series with base on other correlated time series with full data. As the AnEn method benefits from the use of large volumes of data, there is a great interest in improving its efficiency. In this paper, the Principal Component Analysis (PCA) technique is combined with the classical AnEn method and a K-means cluster-based variant, within the context of reconstructing missing meteorological data at a particular station using information from neighboring stations. This combination allows to reduce the dimension of the number of predictor time series, while ensuring better accuracy and higher computational performance than the AnEn methods: it reduces prediction errors by up to 30% and achieves a computational speedup of up to 2x.

Palavras-chave

Meteorological data reconstruction Analogue ensemble K-means clustering Principal component analysis MATLAB R

URI

http://hdl.handle.net/10198/36124

Citação

Breve, Murilo M.; Balsa, Carlos; Rufino, José (2024). Reconstruction of meteorological records with PCA-based analog ensemble methods. In 11th World Conference on Information Systems and Technologies, WorldCIST 2023. ISSN 2367-3370. 799 LNNS, p. 85-96. ISSN 2367-3370. DOI: 10.1007/978-3-031-45642-8_8