ArXiv Preprint
Online changepoint detection aims to detect anomalies and changes in
real-time in high-frequency data streams, sometimes with limited available
computational resources. This is an important task that is rooted in many
real-world applications, including and not limited to cybersecurity, medicine
and astrophysics. While fast and efficient online algorithms have been recently
introduced, these rely on parametric assumptions which are often violated in
practical applications. Motivated by data streams from the telecommunications
sector, we build a flexible nonparametric approach to detect a change in the
distribution of a sequence. Our procedure, NP-FOCuS, builds a sequential
likelihood ratio test for a change in a set of points of the empirical
cumulative density function of our data. This is achieved by keeping track of
the number of observations above or below those points. Thanks to functional
pruning ideas, NP-FOCuS has a computational cost that is log-linear in the
number of observations and is suitable for high-frequency data streams. In
terms of detection power, NP-FOCuS is seen to outperform current nonparametric
online changepoint techniques in a variety of settings. We demonstrate the
utility of the procedure on both simulated and real data.
Gaetano Romano, Idris Eckley, Paul Fearnhead
2023-02-06
