Online Neural Networks for Change-Point Detection
This work addresses change-point detection for applications like industrial monitoring and health systems, but it appears incremental as it builds on known neural network methods.
The authors tackled the problem of detecting change points in time series by presenting two online neural network approaches with linear computational complexity, which outperformed existing methods on synthetic and real-world datasets.
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.