An empirical method to select dominant independent components in ICA for time series analysis

Back and Weigend (1997) showed that the dominant independent components obtained by independent component analysis (ICA) can reveal more underlying structure of the time series than principal component analysis. To find those dominant independent components, all the independent components are listed in an appropriate order and then a subset of components is selected according to the order. However, currently there does not exist a systematic way to choose such a subset. In this paper, we propose a number selection criterion to choose an appropriate dominant number, through which the dominant independent components can be automatically determined from a set of ordered components. Experiments on foreign exchange rates have shown the performance of this empirical method