  Multivariate time series can often have a large number of dimensions, whether
it is due to the vast amount of collected features or due to how the data
sources are processed. Frequently, the main structure of the high-dimensional
time series can be well represented by a lower dimensional subspace. As vast
quantities of data are being collected over long periods of time, it is
reasonable to assume that the underlying subspace structure would change over
time. In this work, we propose a change-point detection method based on
low-rank matrix factorisation that can detect multiple changes in the
underlying subspace of a multivariate time series. Experimental results on both
synthetic and real data sets demonstrate the effectiveness of our approach and
its advantages against various state-of-the-art methods.
