  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.
