Identifying Unique Causal Network from Nonstationary Time Series

• URL: http://arxiv.org/abs/2211.10085v3
• Date: Wed, 30 Aug 2023 00:46:21 GMT
• Title: Identifying Unique Causal Network from Nonstationary Time Series
• Authors: Mingyu Kang and Duxin Chen and Ning Meng and Gang Yan and Wenwu Yu
• Abstract summary: A novel causation model named Unique Causal Network (UCN) is proposed in this paper. UCN considers the influence of time delay, and proves the uniqueness of obtained network structure. A higher-order causal entropy (HCE) algorithm is designed to identify the structure of UCN in a distributed way.
• Score: 15.204794081719097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying causality is a challenging task in many data-intensive scenarios. Many algorithms have been proposed for this critical task. However, most of them consider the learning algorithms for directed acyclic graph (DAG) of Bayesian network (BN). These BN-based models only have limited causal explainability because of the issue of Markov equivalence class. Moreover, they are dependent on the assumption of stationarity, whereas many sampling time series from complex system are nonstationary. The nonstationary time series bring dataset shift problem, which leads to the unsatisfactory performances of these algorithms. To fill these gaps, a novel causation model named Unique Causal Network (UCN) is proposed in this paper. Different from the previous BN-based models, UCN considers the influence of time delay, and proves the uniqueness of obtained network structure, which addresses the issue of Markov equivalence class. Furthermore, based on the decomposability property of UCN, a higher-order causal entropy (HCE) algorithm is designed to identify the structure of UCN in a distributed way. HCE algorithm measures the strength of causality by using nearest-neighbors entropy estimator, which works well on nonstationary time series. Finally, lots of experiments validate that HCE algorithm achieves state-of-the-art accuracy when time series are nonstationary, compared to the other baseline algorithms.

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