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 |
Metadata
Title
A local correlation integral method for outlier detection in spatially correlated functional dataDate
2023Publisher
SpringerISSN
1436-3240; 1436-3259Bibliographic citation
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)Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s00477-023-02624-9Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Stochastic Environmental Research and Risk Assessment, 2024, 38, p. 1197-1211Rights
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
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info:eu-repo/semantics/restrictedAccess
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- MAT_Articles [762]