Abstract
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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Notes
The EEG data is publicly available from https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State
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Acknowledgements
This research was supported in part by National Key R&D Program of China (2021ZD0111501), National Science Fund for Excellent Young Scholars (62122022), Natural Science Foundation of China (61876043, 61976052), Science and Technology Planning Project of Guangzhou (201902010058), Guangdong Provincial Science and Technology Innovation Strategy Fund (2019B121203012). Wei Chen was supported by China Postdoctoral Science Foundation (2021M690734).
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Cai, R., Huang, L., Chen, W. et al. Learning dynamic causal mechanisms from non-stationary data. Appl Intell 53, 5437–5448 (2023). https://doi.org/10.1007/s10489-022-03843-3
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DOI: https://doi.org/10.1007/s10489-022-03843-3