Application of signatures for forecasting
| Témavezető: | Tikosi Kinga |
| Alfréd Rényi Institute of Mathematics | |
| email: | tikosi@staff.elte.hu |
Projekt leírás
A signature is a path-transform from stochastic analysis that enjoys a certain universal approximation property. This univer- sality allows to calibrate signatures in a fast and efficient way to a wide class of problems with nonlinear dependencies. The goal of the project is to understand this approach and use it for forecasting a chosen time-series (financial data or other).
Előfeltételek
- familiarity with Python
- having learned or currently learning basic stochastic analysis or stochastic processes (the concept of a stochastic differential equation, Brownian motion),
- interest in financial mathematics or data analysis
- being able to understand scientific literature in English
Hivatkozások
- Daniel Levin, Terry Lyons, and Hao Ni. Learning from the past, predicting the statistics for the future, learning an evolving system. arXiv preprint arXiv:1309.0260, 2013.
- Ioannis Karatzas and Steven E Shreve. Brownian motion and stochastic cal- culus. In Brownian Motion and Stochastic Calculus, pages 47–127. Springer, 1998.
- Imanol Perez Arribas, Cristopher Salvi, and Lukasz Szpruch. Sig-sdes model for quantitative finance. arXiv preprint arXiv:2006.00218, 2020.
- Ilya Chevyrev and Andrey Kormilitzin. A primer on the signature method in machine learning. arXiv preprint arXiv:1603.03788, 2016.
- Lajos Gergely Gyurkó, Terry Lyons, Mark Kontkowski, and Jonathan Field. Extracting information from the signature of a financial data stream. arXiv preprint arXiv:1307.7244, 2013.