We assume a second-order source separation model where the observed
multivariate time series is a linear mixture of latent, temporally
uncorrelated time series with some components pure white noise. To avoid
the modelling of noise, we extract the non-noise latent components
using some standard method, allowing the modelling of the extracted
univariate time series individually. An important question is the
determination of which of the latent components are of interest in
modelling and which can be considered as noise. Bootstrap-based methods
have recently been used in determining the latent dimension in various
methods of unsupervised and supervised dimension reduction and we
propose a set of similar estimation strategies for second-order
stationary time series. Simulation studies and a sound wave example are
used to show the method’s effectiveness.