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.

翻译：复杂网络通过一组异常时间序列之间的相互作用展现系统动态。针对滑动窗口中高度相关时间序列对的相关系数计算问题，有效计算并更新用户自定义滑动周期与阈值下的相关系数矩阵，能够支持大规模时间序列网络动态分析。本文提出Dangoron框架，用于在滑动窗口中高效识别高度相关的时间序列对并计算其精确相关系数。通过预测跨滑动窗口的动态相关性并剪枝无关时间序列，Dangoron的运行速度比基线方法至少快一个数量级。此外，我们提出相关系数矩阵计算问题的首个基准测试工具Tomborg。