Component analysis in financial time series

We discuss the application of principal component analysis and independent component analysis for blind source separation of univariate financial time series. In order to perform single-channel versions of these techniques, we work within the embedding framework, using delay coordinate vectors to obtain a multidimensional representation of the system dynamics at each time instance. The main objective is to find out if these techniques are able to perform feature extraction, signal-noise-decomposition and dimensionality reduction, since that would enable a further inside look into the behaviour and mechanics of financial markets. Both methods are applied to the currency exchange rate data of the British Pound against the US Dollar.