  Moments when a time series changes its behaviour are called change points.
Detection of such points is a well-known problem, which can be found in many
applications: quality monitoring of industrial processes, failure detection in
complex systems, health monitoring, speech recognition and video analysis.
Occurrence of change point implies that the state of the system is altered and
its timely detection might help to prevent unwanted consequences. In this
paper, we present two online change-point detection approaches based on neural
networks. These algorithms demonstrate linear computational complexity and are
suitable for change-point detection in large time series. We compare them with
the best known algorithms on various synthetic and real world data sets.
Experiments show that the proposed methods outperform known approaches.
