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Related Experiment Video

Updated: May 24, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation.

Baoyu Jing¹, Dawei Zhou², Kan Ren³

¹University of Illinois, Urbana-Champaign, IL, USA.

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management

|March 5, 2025

Summary

This summary is machine-generated.

This study introduces Casper, a novel method for spatiotemporal time series imputation that uses causality to avoid overfitting. Casper effectively imputes missing data by focusing on causal relationships, outperforming existing techniques.

Keywords:

Causal Attention Spatiotemporal Graph Neural Network Spatiotemporal Time Series Imputation

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Area of Science:

Data Science
Machine Learning
Causal Inference

Background:

Spatiotemporal time series data often suffer from missing values due to sensor failures.
Existing imputation methods may overfit by using non-causal correlations introduced by confounders.

Purpose of the Study:

To propose a causality-based approach for spatiotemporal time series imputation.
To develop a novel neural network model that accounts for causal relationships.

Main Methods:

Revisiting spatiotemporal imputation from a causal perspective using frontdoor adjustment.
Introducing the Causality-Aware Spatiotemporal Graph Neural Network (Casper) with a Prompt Based Decoder (PBD) and Spatiotemporal Causal Attention (SCA).

Main Results:

Casper effectively reduces the impact of confounders and identifies sparse causal relationships.
Theoretical analysis shows SCA discovers causal links via gradient values.
Experimental results demonstrate Casper's superior performance over baseline methods on real-world datasets.

Conclusions:

Casper provides an effective and robust solution for spatiotemporal time series imputation by leveraging causal inference.
The model successfully mitigates overfitting issues caused by non-causal correlations.