  In stationary subspace analysis (SSA) one assumes that the observable
p-variate time series is a linear mixture of a k-variate nonstationary time
series and a (p-k)-variate stationary time series. The aim is then to estimate
the unmixing matrix which transforms the observed multivariate time series onto
stationary and nonstationary components. In the classical approach multivariate
data are projected onto stationary and nonstationary subspaces by minimizing a
Kullback-Leibler divergence between Gaussian distributions, and the method only
detects nonstationarities in the first two moments. In this paper we consider
SSA in a more general multivariate time series setting and propose SSA methods
which are able to detect nonstationarities in mean, variance and
autocorrelation, or in all of them. Simulation studies illustrate the
performances of proposed methods, and it is shown that especially the method
that detects all three types of nonstationarities performs well in various time
series settings. The paper is concluded with an illustrative example.
