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A GNN-based time-frequency cooperated causal inference method for irregular time series
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DOI：10.23919/jcc.fa.2025-0282.202604.png)
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Abstract
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Causal inference refers to the discovery and acquisition of causal relationships from observational data, which is an important way for Internet of Things (IoT) systems to realize from perception to cognition. However, most existing causal inference methods assume that the input data is structured. For irregular time series with random missing items, the absence of important time points often leads to serious degradation of causal inference performance, which hinders practical applications. To this end, a GNN-based time-frequency cooperated causal inference (TFCI-GNN) method for irregular time series is proposed. The temporal features are initially extracted using a temporal encoder, and the frequency domain encoder adaptively models the frequency interdependence between channels using discrete cosine transform (DCT), forming attention coefficients that act on the temporal features. By utilizing the estimated adjacency matrix, feature aggregation is performed using a GNN. Finally, the causal graph is decoded and updated using a unique self-supervised approach, which mutually promotes the process of interpolation and causal inference. Experimental results on synthetic and real datasets show that the proposed TFCI-GNN method outperforms the baseline algorithms in inference performance.
Keywords:
causal inference
Internet of Things (IoT)
irregular time series
graph neural network
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