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Lecture notes on rough paths and applications to machine learning

Year: 2024

Type: preprint

Abstract: These notes expound the recent use of the signature transform and rough path theory in data science and machine learning. We develop the core theory of the signature from first principles and then survey some recent popular applications of this approach, including signature-based kernel methods and neural rough differential equations. The notes are based on a course given by the two authors at Imperial College London.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2404.06583

Authors: Cass, Thomas, Salvi, Cristopher

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4394777927

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