5 papers
How Fast Do Signatures Learn? Statistical Theory and Applications for Path Regression
Blanka Horvath, Wen Su, Wu Su +2
Many prediction and decision-making problems in operations research involve path-valued covariates -- data that evolve over time -- for which path signatures have become a canonica…
Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains
Raeid Saqur, Christoph Bergmeir, Blanka Horvath +3
We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonaliti…
Kernel Learning for Mean-Variance Trading Strategies
Owen Futter, Nicola Muca Cirone, Blanka Horvath
In this article, we develop a kernel-based framework for constructing dynamic, pathdependent trading strategies under a mean-variance optimisation criterion. Building on the theore…
Signature Maximum Mean Discrepancy Two-Sample Statistical Tests
Andrew Alden, Blanka Horvath, Zacharia Issa
Maximum Mean Discrepancy (MMD) is a widely used concept in machine learning research which has gained popularity in recent years as a highly effective tool for comparing (finite-di…
Scalable Signature-Based Distribution Regression via Reference Sets
Andrew Alden, Carmine Ventre, Blanka Horvath
Distribution Regression (DR) on stochastic processes describes the learning task of regression on collections of time series. Path signatures, a technique prevalent in stochastic a…