RT Generic
T1 Multivariate Functional Outlier Detection using the FastMUOD Indices
A1 Ojo, Oluwasegun Taiwo
A1 Fernández Anta, Antonio
A1 Genton, Marc G.
A1 Lillo Rodríguez, Rosa Elvira
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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.
SN 2387-0303
YR 2022
FD 2022-09-09
LK https://hdl.handle.net/10016/35665
UL https://hdl.handle.net/10016/35665
LA eng
DS e-Archivo
RD 27 jul. 2024