GAA-UAM/scikit-fda

What it solves

scikit-fda provides tools for Functional Data Analysis (FDA), a branch of statistics that deals with data depending on a continuous parameter. It allows users to analyze, represent, and preprocess functional data, moving beyond traditional discrete data analysis.

How it works

The package provides a suite of classes, methods, and functions designed to handle functional data. It integrates with the scientific Python ecosystem (such as NumPy, SciPy, and scikit-learn) to perform tasks such as:

• Exploratory Analysis: Understanding the patterns within functional data.
• Preprocessing: Preparing functional data for further analysis.
• Inference and Modeling: Performing regression, classification, and clustering specifically for functional data.

Who it’s for

It is designed for statisticians, data scientists, and researchers who work with data that is continuous in nature (e.g., time series, growth curves, or spectral data) and need specialized tools for the analysis of such data in Python.

Highlights

• Comprehensive FDA Toolkit: Includes tools for representation, preprocessing, and exploratory analysis.
• ML Integration: Supports functional classification, regression, and clustering.
• Inference Support: Provides methods for statistical inference on functional data.
• Scientific Python Ecosystem: Built on top of NumPy, SciPy, and scikit-learn.