Path Signatures in Machine Learning-based Analysis. Of Financial Time Series

      Аннотация: 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.

Ключевые слова: financial time series, machine learning, Path Signatures, Stock Index Prices, volatility