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Complex networks represent system dynamics through the interactions of a setof anomalous time series. Consider the problem of computing correlations forhighly correlated pairs of time series across sliding windows. Efficientlycomputing and updating the correlation matrix for user-defined sliding periodsand thresholds enables large-scale time series network dynamics analysis. Weintroduce Dangoron, a framework for effectively identifying highly correlatedpairs of time series over sliding windows and computing their exactcorrelation. By predicting dynamic correlation across sliding windows andpruning unrelated time series, Dangoron is at least an order of magnitudefaster than a baseline. Additionally, we propose Tomborg, the first benchmarkfor the problem of correlation matrix computation.

Dangoron: Network Construction on Large-scale Time Series Data across Sliding Windows