Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks

TL;DR

This paper introduces a correlation-aware unsupervised change-point detection method using graph neural networks to explicitly model variable dependencies and dynamics in multivariate time series, improving detection accuracy.

Contribution

It presents a novel GNN-based model that captures correlation structures and their dynamics for more effective change-point detection in multivariate data.

Findings

Outperforms strong baselines on synthetic and real datasets

Effectively classifies change-points as correlation or independent changes

Demonstrates the importance of modeling variable dependencies in CPD

Abstract

Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods either ignore the dependency structures entirely or rely on the (unrealistic) assumption that the correlation structures are static over time. In this paper, we propose a Correlation-aware Dynamics Model for CPD, which explicitly models the correlation structure and dynamics of variables by incorporating graph neural networks into an encoder-decoder framework. Extensive experiments on synthetic and real-world datasets demonstrate the advantageous performance of the proposed model on CPD tasks over strong baselines, as well as its ability to classify the change-points as correlation changes or independent changes. Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks