4.5 Article

Learning dynamic causal mechanisms from non-stationary data

Journal

APPLIED INTELLIGENCE
Volume 53, Issue 5, Pages 5437-5448

Publisher

SPRINGER
DOI: 10.1007/s10489-022-03843-3

Keywords

Time-series; Non-stationary; Causal discovery; Dynamic causal mechanisms; Temporal abstraction

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In this paper, we propose a Gaussian-based Variational Temporal Abstraction model (GVTA) to detect and learn non-stationary causal mechanisms from multiple time series. Our method utilizes a hierarchical cyclic state-space model to detect stationary states and uses a Gaussian process algorithm to estimate the causal mechanisms for each stationary state. Experimental results demonstrate the correctness and effectiveness of our method.
Causal discovery from non-stationary time series is an important but challenging task. Most existing non-stationary approaches only consider the changes of causal coefficients, which are merely satisfied in real-world scenarios. In this paper, we introduce a Gaussian-based Variational Temporal Abstraction model (GVTA) to detect and learn non-stationary causal mechanisms from multiple time series. First, we utilize a hierarchical cyclic state-space model to detect the stationary states from the non-stationary time series. Second, we use the Gaussian process algorithm to estimate the causal mechanism for each stationary state. Experimental results on both simulation and real-world data demonstrate the correctness and effectiveness of our method.

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