This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: 'Can one improve upon the state of the art in financial time series analysis through the application of machine learning?' The work is split according to the following time series trichotomy: 1) characterization - determine the fundamental properties of the time series; 2) modelling - find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting - accurately predict the short-term evolution of the system.