| 要約: | Causal discovery from multivariate time-series is a significant and fundamental problem in numerous disciplines.The existing multivariate time-series causal discovery methods learn the causal relations for each individual while some individuals may share the same causal relations;therefore, they may exploit data insufficiently.To this end, this study proposes a collective causal discovery algorithm for multivariate time-series, which is a two-stage algorithm.The first stage measures the similarity of individuals from the perspective of causal relations and clusters the individuals into different groups based on similarity without assigning the number of groups.The second stage involves learning the collective causal relations for each group using variational inference, which sufficiently utilizes the data of individuals in the same group.The experimental result shows that the proposed method outperforms existing methods on simulated data, and the AUC scores are improved by 5%-20%.On real data, the proposed algorithm can separate groups with different causal relations and determine the difference in causal relations for each group, which illustrates the capability of the proposed algorithm in causal discovery and multivariate time-series clustering.
|
|---|
