Multivariate time series forecasting using independent component analysis

The paper presents a method for multivariate time series forecasting using independent component analysis, as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then transforming back to the original time series. The forecasting can be done separately and with a different method for each component, depending on its time structure. The method has been applied in simulation to an artificial multivariate time series with five components, generated from three sources and a mixing matrix, randomly generated, and to a multivariate financial time series having as a components US, UK, West Germany and Japan bond yield daily.