Path Signatures in Machine Learning-based Analysis

This paper examines the application of the rough paths theory in modelling of financial time series. The theory of rough paths provides a way to effectively and efficiently capture the relevant information about rough signals which can be used in machine learning modelling. This approach is applied to twelve stock market indexes with a goal to predict the sign of their daily returns (positive or negative) and their realized daily volatility.