2023/04/11 by Yunlong Xu, Xu, Yunlong, Peizhen Yang +3
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Databases (cs.DB) #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2304.12085
openalex publication_date 2023/04/11 · openalex created_date 2023/04/27 · openalex updated_date 2026/07/28
Complex networks represent system dynamics through the interactions of a set of anomalous time series. Consider the problem of computing correlations for highly correlated pairs of time series across sliding windows. Efficiently computing and updating the correlation matrix for user-defined sliding periods and thresholds enables large-scale time series network dynamics analysis. We introduce Dangoron, a framework for effectively identifying highly correlated pairs of time series over sliding windows and computing their exact correlation. By predicting dynamic correlation across sliding windows and pruning unrelated time series, Dangoron is at least an order of magnitude faster than a baseline. Additionally, we propose Tomborg, the first benchmark for the problem of correlation matrix computation.