Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation (2403.11960v4)

Abstract: Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechanical damages and Internet outages. Imputing the missing values is crucial for analyzing time series. When recovering a specific data point, most existing methods consider all the information relevant to that point regardless of the cause-and-effect relationship. During data collection, it is inevitable that some unknown confounders are included, e.g., background noise in time series and non-causal shortcut edges in the constructed sensor network. These confounders could open backdoor paths and establish non-causal correlations between the input and output. Over-exploiting these non-causal correlations could cause overfitting. In this paper, we first revisit spatiotemporal time series imputation from a causal perspective and show how to block the confounders via the frontdoor adjustment. Based on the results of frontdoor adjustment, we introduce a novel Causality-Aware Spatiotemporal Graph Neural Network (Casper), which contains a novel Prompt Based Decoder (PBD) and a Spatiotemporal Causal Attention (SCA). PBD could reduce the impact of confounders and SCA could discover the sparse causal relationships among embeddings. Theoretical analysis reveals that SCA discovers causal relationships based on the values of gradients. We evaluate Casper on three real-world datasets, and the experimental results show that Casper could outperform the baselines and could effectively discover causal relationships.

• The paper introduces a novel causality-aware GNN model that leverages spatiotemporal causal attention to effectively identify and utilize genuine cause-effect relationships in sensor data.
• It employs a prompt-based decoder to incorporate global contextual information while mitigating the adverse effects of confounders on imputation accuracy.
• Experimental results on real-world datasets demonstrate superior performance in reducing MAE and MSE compared to traditional deep learning approaches.

Exploring the Causality in Spatiotemporal Time Series Imputation with Graph Neural Networks

Introduction

Spatiotemporal time series data, obtained from sensor networks monitoring various phenomena, often suffer from missing values due to sensor malfunctions or other disruptions. The imputation of these missing values is crucial for subsequent data analysis and decision-making processes. Traditional methods and most existing deep learning approaches do not differentiate between causal and non-causal relationships when attempting imputation, potentially leveraging spurious correlations introduced by confounders.

In addressing these challenges, Jing et al. propose a novel approach titled Causality-Aware Spatiotemporal Graph Neural Network (). This method is grounded in a causal perspective, identifying and leveraging the cause-and-effect relationships intrinsic to spatiotemporal data. The model incorporates a Spatiotemporal Causal Attention (SCA) mechanism and a Prompt Based Decoder (PBD), providing a robust framework against confounders and emphasizing causal relationships for imputation.

Methodology

** revisits the spatiotemporal time series imputation problem through a causal lens, explicitly modeling the interactions between input, output, embeddings, and confounders using the Structure Causal Model (SCM). The work highlights the detrimental role of confounders in creating spurious correlations and addresses them via the frontdoor adjustment, effectively disentangling causal relationships from non-causal correlations.

The architecture of comprises two main components:

• Spatiotemporal Causal Attention (SCA): This mechanism discovers and utilizes sparse causal relationships among time series embeddings, fundamentally based on gradients, which inherently filters out the non-causal correlations.
• Prompt Based Decoder (PBD): Contrary to directly approximating the entire context for imputation, PBD employs learnable prompts to encapsulate the dataset's global contextual information, effectively mitigating the influence of confounders.

Theoretical Insights

The paper provides a solid theoretical foundation for the SCA mechanism's ability to discern causal from non-causal relationships, relying on gradients' values. This approach not only simplifies the interpretation of causal relations but also enhances the model's focus on genuinely influential data points, thus improving imputation accuracy and model robustness.

Experimental Evaluation

Extensively evaluated on three real-world datasets, showcases superior performance over existing baselines in terms of MAE and MSE metrics. These strong numerical results underline 's efficacy in leveraging causal relationships for imputation tasks, even in the presence of confounders.

Future Directions

The introduction of causality into the imputation of spatiotemporal time series opens new avenues for research, including the potential for discovering more complex causal mechanisms within sensor networks and extending these concepts to other domains where cause-and-effect relationships play a crucial role. Furthermore, the integration of causality could provide a new paradigm for designing more robust and interpretable machine learning models across various applications.

Conclusion

addresses the critical issue of confounders in spatiotemporal time series imputation by innovatively applying causality theory. Through its causality-aware architecture, it not only achieves superior imputation performance but also provides a pathway toward understanding the underlying cause-and-effect relationships in sensor network data. This work represents a significant step forward in the integration of causality with graph neural networks, offering insights that could transform future approaches in spatiotemporal data analysis and beyond.