Daniil Shmelev

Abstract

Central to rough path theory is the signature transform of a path, an infinite series of tensors given by the iterated integrals of the underlying path. The signature poses an effective way to capture sequentially ordered information, thanks both to its rich analytic and algebraic properties as well as its universality when used as a basis to approximate functions on path space. Whilst a truncated version of the signature can be efficiently computed using Chen's identity, there is a lack of efficient methods for computing a sparse collection of iterated integrals contained in high levels of the signature. We address this problem by leveraging signature kernels, defined as the inner product of two signatures, and computable efficiently by means of PDE-based methods. By forming a filter in signature space with which to take kernels, one can effectively isolate specific groups of signature coefficients and, in particular, a singular coefficient at any depth of the transform. We show that such a filter can be expressed as a linear combination of suitable signature transforms and demonstrate empirically the effectiveness of our approach. To conclude, we give an example use case for sparse collections of signature coefficients based on the construction of N-step Euler schemes for sparse CDEs. 

Our speaker

Daniil is a PhD student at the Oxford-Imperial CDT in Statistics and Machine Learning, under the supervision of Dr. Cristopher Salvi. Prior to this, he completed an MSc in Mathematics and Finance at Imperial College London and a BA in Mathematics at Cambridge University. His research interests are centred around the applications of Rough Paths Theory to Machine Learning and the analysis of financial time-series.

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