2014/10/03 by Ian Barnett, Barnett, Ian, Jukka‐Pekka Onnela +1 · 1 citation
Economics, Econometrics and Finance · Neuroscience · Psychology · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Mental Health Research Topics #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1410.0761
openalex publication_date 2014/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves in time, it is useful to determine the points in time where the network structure changes significantly as these may correspond also to functional change points. We propose a method for detecting these change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires no distributional assumptions. We investigate the difficulty of change point detection near the boundaries of data in correlation networks and demonstrate the power of our method and a competing method through simulation. We also show the generalizable nature of our method by applying it to stock price data as well as fMRI data.