# A Geometric Approach to Visualization of Variability in Functional Data

## TL;DR

This paper introduces a new geometric method for visualizing variability in functional data by decomposing it into amplitude, phase, and translation components, and creating specialized boxplot-like displays for each.

## Contribution

It presents a novel framework using square-root slope functions to decompose and visualize functional data variability, including new outlier detection and visualization tools.

## Key findings

- Effective visualization of functional variability demonstrated on real data
- New outlier detection method based on component decomposition
- Validated through extensive simulations

## Abstract

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves.