Robust change detection via information projection

Can Emre Koksal

Abstract:

We study the robust transient and quickest change detection problems with unknown post-change distributions.
When the distribution after the change point is unknown, the change in the distribution of observations may occur
in multiple ways without much structure on the observations, whereas, before the change point, a false alarm is
highly structured, following a particular sample path. We first characterize these likely events for the deviation and
propose a method to test the empirical distribution, relative to the most likely way for it to occur as an outlier. We
benchmark our method with the finite moving average (FMA) method, generalized likelihood ratio test (GLRT)
and M-statistic kernel (MSK) change point detection method under 4 different performance criteria including the
run time complexity. Finally, we apply our method on economic market indicators and climate data. Our method
successfully captures the regime shifts during times of historical significance for the markets and identifies the
current climate change phenomenon to be a highly likely regime shift rather than a random event.