Multivariate Functional Data Visualization and Outlier Detection
Not on the map. Why?
Abstract (arXiv)
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 3.
5 papers.
- 2022scikit-fda: A Python Package for Functional Data Analysiscited by 12
- 2022Functional Anomaly Detection: a Benchmark Studycited by 10
- 2021A geometric perspective on functional outlier detectioncited by 2
- 2021On projection methods for functional time series forecastingcited by 1
- 2022A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Datacited by 1
- 2022scikit-fda: A Python Package for Functional Data Analysis4 Nov 2022
- 2022A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data11 Feb 2022
- 2022Functional Anomaly Detection: a Benchmark Study13 Jan 2022
- 2021A geometric perspective on functional outlier detection14 Sep 2021
- 2021On projection methods for functional time series forecasting10 May 2021