A local correlation integral method for outlier detection in spatially correlated functional data
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
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https://doi.org/10.1007/s00477-023-02624-9 |
Metadatos
Título
A local correlation integral method for outlier detection in spatially correlated functional dataFecha de publicación
2023Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
SOSA, Jorge, et al. A local correlation integral method for outlier detection in spatially correlated functional data. Stochastic Environmental Research and Risk Assessment, 38, 1197–1211 (2024)Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-023-02624-9Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper proposes a new methodology for detecting outliers in spatially correlated functional data. We use a Local Correlation Integral (LOCI) algorithm substituting the Euclidean distance calculation by the Hilbert ... [+]
This paper proposes a new methodology for detecting outliers in spatially correlated functional data. We use a Local Correlation Integral (LOCI) algorithm substituting the Euclidean distance calculation by the Hilbert space
distance weighted by the semivariogram, obtaining a weighted dissimilarity metric among the geo-referenced curves, which takes into account the spatial correlation structure. In addition, we also consider the distance proposed in Romano et al. (2020), which optimizes the distance calculation for spatially dependent functional data. A simulation study is conducted to evaluate the performance of the proposed methodology. We analyze the role of a threshold value appearing as an hyperparameter in our approach, and show that our distance weighted by the semivariogram is overall superior to the other types of distances considered in the study. We analyze time series of Land Surface Temperature (LST) data in the region of Andalusia (Spain), detecting significant outliers that would have not been detected using other procedures. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment, 2024, 38, p. 1197-1211Derechos de acceso
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
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