# scikit-fda: A Python Package for Functional Data Analysis

arXiv: https://arxiv.org/abs/2211.02566 · HTML: https://mlsanity.com/paper/2211.02566/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 102 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Functional linear regression with truncated signatures](https://mlsanity.com/paper/2006.08442/) (2020)
- [The mRMR variable selection method: a comparative study for functional data](https://mlsanity.com/paper/1507.03496/) (2015)
- [Multivariate Functional Data Visualization and Outlier Detection](https://mlsanity.com/paper/1703.06419/) (2017)
- [Benchmarking time series classification -- Functional data vs machine learning approaches](https://mlsanity.com/paper/1911.07511/) (2019)
- [On the use of reproducing kernel Hilbert spaces in functional classification](https://mlsanity.com/paper/1507.04398/) (2015)
- [Directional Outlyingness for Multivariate Functional Data](https://mlsanity.com/paper/1612.04615/) (2016)
- [Variable selection in functional data classification: a maxima-hunting proposal](https://mlsanity.com/paper/1309.6697/) (2013)
- [Feature selection in functional data classification with recursive maxima hunting](https://mlsanity.com/paper/1806.02922/) (2018)
- [FDApy: a Python package for functional data](https://mlsanity.com/paper/2101.11003/) (2021)
- [Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis Systems](https://mlsanity.com/paper/2107.14728/) (2021)

## Built on by (most cited first, 12 of 12)

- [Nonlinear functional regression by functional deep neural network with kernel embedding](https://mlsanity.com/paper/2401.02890/) (2024)
- [Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation](https://mlsanity.com/paper/2505.17103/) (2025)
- [Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes](https://mlsanity.com/paper/2403.16377/) (2024)
- [3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces](https://mlsanity.com/paper/2404.07748/) (2024)
- [Functional Diffusion Maps](https://mlsanity.com/paper/2304.14378/) (2023)
- [Functional relevance based on the continuous Shapley value](https://mlsanity.com/paper/2411.18575/) (2024)
- [Empirical risk minimization algorithm for multiclass classification of S.D.E. paths](https://mlsanity.com/paper/2503.14045/) (2025)
- [Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed](https://mlsanity.com/paper/2508.16686/) (2025)
- [StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds](https://mlsanity.com/paper/2402.19287/) (2024)
- [Persistence Spheres: Bi-continuous Representations of Persistence Diagrams](https://mlsanity.com/paper/2509.16999/) (2025)
- [Near-optimal Delta-convex Estimation of Lipschitz Functions](https://mlsanity.com/paper/2511.15615/) (2025)
- [Gaussian mixture models in Hilbert spaces via kernel methods](https://mlsanity.com/paper/2605.05996/) (2026)

## Abstract (arXiv)

The library scikit-fda is a Python package for Functional Data Analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in Python's scientific ecosystem. In particular, it conforms to the scikit-learn application programming interface so as to take advantage of the functionality for machine learning provided by this package: pipelines, model selection, and hyperparameter tuning, among others. The scikit-fda package has been released as free and open-source software under a 3-Clause BSD license and is open to contributions from the FDA community. The library's extensive documentation includes step-by-step tutorials and detailed examples of use.