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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.