  Standard online change point detection (CPD) methods tend to have large false
discovery rates as their detections are sensitive to outliers. To overcome this
drawback, we propose Greedy Online Change Point Detection (GOCPD), a
computationally appealing method which finds change points by maximizing the
probability of the data coming from the (temporal) concatenation of two
independent models. We show that, for time series with a single change point,
this objective is unimodal and thus CPD can be accelerated via ternary search
with logarithmic complexity. We demonstrate the effectiveness of GOCPD on
synthetic data and validate our findings on real-world univariate and
multivariate settings.
