  Change detection in multivariate time series has applications in many
domains, including health care and network monitoring. A common approach to
detect changes is to compare the divergence between the distributions of a
reference window and a test window. When the number of dimensions is very
large, however, the naive approach has both quality and efficiency issues: to
ensure robustness the window size needs to be large, which not only leads to
missed alarms but also increases runtime.
  To this end, we propose LIGHT, a linear-time algorithm for robustly detecting
non-linear changes in massively high dimensional time series. Importantly,
LIGHT provides high flexibility in choosing the window size, allowing the
domain expert to fit the level of details required. To do such, we 1) perform
scalable PCA to reduce dimensionality, 2) perform scalable factorization of the
joint distribution, and 3) scalably compute divergences between these lower
dimensional distributions. Extensive empirical evaluation on both synthetic and
real-world data show that LIGHT outperforms state of the art with up to 100%
improvement in both quality and efficiency.
