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).
Referenciák
- 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.