Abstract: The problem of linearly decomposing stochastic processes into 'independent' component processes is addressed. In contrast to ordinary independent component analysis, the time structure of the components is taken into account. It is shown that the data model of independent component analysis is identifiable if the innovation processes of the latent components (source signals) are independent. The results show the utility of performing independent component analysis on the innovation process instead of the original data.