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.
|Original language||English (US)|
|State||Published - Mar 28 2023|
Bibliographical noteKAUST Repository Item: Exported on 2023-04-04
Acknowledgements: This work has been partially supported by the Comunidad de Madrid grant Edge Data-CM (P2018/TCS4499, cofunded by FSE & FEDER) and the Ministerio de Economía, Industria y competitividad, Gobierno de España, grant number PID2019-104901RB-I00. The work is also part of the agreements of the Community of Madrid (Ministry of Education, Universities, Science and Spokesperson) with the Carlos III University of Madrid and the IMDEA Networks Institute for the funding of research projects on SARS-CoV-2 and COVID-19 disease, project names “Multi-source and multi-method prediction to support COVID-19 policy decision making" and “COVID-19 Monitoring via Data-Intensive Analysis", which are supported with REACT-EU funds from the European regional development fund “a way of making Europe". Marc G. Genton and OluwasegunOjo’s research was supported by the King Abdullah University of Science and Technology (KAUST).