Inferring Dynamic Hidden Graph Structure in Heterogeneous Correlated Time Series
异质相关时间序列中的动态隐含图结构推断
Modeling heterogeneous correlated time series requires the ability to learn hidden dynamic relationships between component time series with possibly varying periodicities and generative processes. To address this challenge, we formulate and evaluate a windowed variance-correlation metric (WVC) designed to quantify time-varying correlations between signals. This method directly recovers hidden relationships in an specified time interval as a weighted adjacency matrix,…