Robust Online Detection in Serially Correlated Directed Network

TL;DR

This paper introduces a robust online detection algorithm for serially correlated directed networks, utilizing a transition probability matrix and an adaptive CUSUM control chart, validated through simulations and real data.

Contribution

It develops a novel adaptive CUSUM method for detecting changes in directed networks without relying on parametric models, handling serial correlation effectively.

Findings

The proposed method outperforms existing techniques in simulations.

It successfully detects anomalies in metro transportation data.

The approach is robust to serial correlation and data standardization.

Abstract

As the complexity of production processes increases, the diversity of data types drives the development of network monitoring technology. This paper mainly focuses on an online algorithm to detect serially correlated directed networks robustly and sensitively. First, we consider a transition probability matrix to resolve the double correlation of primary data. Further, since the sum of each row of the transition probability matrix is one, it standardizes the data, facilitating subsequent modeling. Then we extend the spring length based method to the multivariate case and propose an adaptive cumulative sum (CUSUM) control chart on the strength of a weighted statistic to monitor directed networks. This novel approach assumes only that the process observation is associated with nearby points without any parametric time series model, which is in line with reality. Simulation results and a real example from metro transportation demonstrate the superiority of our design.