  We performed an empirical comparison of ICA and PCA algorithms by applying
them on two simulated noisy time series with varying distribution parameters
and level of noise. In general, ICA shows better results than PCA because it
takes into account higher moments of data distribution. On the other hand, PCA
remains quite sensitive to the level of correlations among signals.
