# Outlier Detection for Functional Data with R Package fdaoutlier

arXiv: https://arxiv.org/abs/2105.05213 · HTML: https://mlsanity.com/paper/2105.05213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 2 of 2)

- [A geometric perspective on functional outlier detection](https://mlsanity.com/paper/2109.06849/) (2021)
- [Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning](https://mlsanity.com/paper/2309.11067/) (2023)

## Abstract (arXiv)

Outlier detection is one of the standard exploratory analysis tasks in functional data analysis. We present the R package fdaoutlier which contains implementations of some of the latest techniques for detecting functional outliers. The package makes it easy to detect different types of outliers (magnitude, shape, and amplitude) in functional data, and some of the implemented methods can be applied to both univariate and multivariate functional data. We illustrate the main functionality of the R package with common functional datasets in the literature.