scikit-fda: Computational Tools for Machine Learning with Functional Data

Carlos Ramos-Carreño, José Luis Torrecilla, Yujian Hong, Alberto Suárez

Abstract: Machine learning from functional data poses particular challenges that require specific computational tools that take into account their structure. In this work, we present scikit-fda, a Python library for functional data analysis, visualization, preprocessing, and machine learning. The library is designed for smooth integration in the Python scientific ecosystem. In particular, it complements and can be used in combination with scikit-learn, the reference Python library for machine learning. The functionality of scikit-fda is illustrated in clustering, regression, and classification problems from different areas of application.