What question did this study set out to answer?

To develop a framework for detecting outliers in functional data while maintaining data integrity.

February 19, 2026EntropyOpen Access

Outlier Detection in Functional Data Using Adjusted Outlyingness

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To develop a framework for detecting outliers in functional data while maintaining data integrity.
Introduced a novel low-dimensional projection for functional data.
Implemented a tailored weighting scheme to preserve curve variations.
Used Mahalanobis distance for detecting directional outlyingness under non-Gaussian assumptions.
Employed robust bootstrap resampling with data-driven thresholding for detection.
Demonstrated higher true positive rates and lower false positive rates for various anomaly types.
Validated through simulation studies with diverse outlier scenarios.
Confirmed practical utility in applications like environmental monitoring and trajectory analysis.

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Anomaly detection framework identifies deviations in functional data, suggesting improvements for data integrity.

Feng et al. (2026) studied this question.

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