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Multivariate Functional Outlier Detection using the FastMUOD Indices

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URI: https://hdl.handle.net/10016/35665
ISSN: 2387-0303
UXXI: DT/0000002023
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ws202209.pdf (5.21 MB)
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2022-09-09
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We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying FastMUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.
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Fastmuod, Functional Data, Functional Outlier Detection, Multivariate Functional Data, Outlier Classification, Video Data
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DES - Working Papers. Statistics and Econometrics. WS