Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data (2309.05305v3)

Abstract: Multivariate Time-Series (MTS) data is crucial in various application fields. With its sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits Spatial-Temporal (ST) dependencies, involving temporal correlations between timestamps and spatial correlations between sensors in each timestamp. To effectively leverage this information, Graph Neural Network-based methods (GNNs) have been widely adopted. However, existing approaches separately capture spatial dependency and temporal dependency and fail to capture the correlations between Different sEnsors at Different Timestamps (DEDT). Overlooking such correlations hinders the comprehensive modelling of ST dependencies within MTS data, thus restricting existing GNNs from learning effective representations. To address this limitation, we propose a novel method called Fully-Connected Spatial-Temporal Graph Neural Network (FC-STGNN), including two key components namely FC graph construction and FC graph convolution. For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT. Further, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations. Extensive experiments show the effectiveness of FC-STGNN on multiple MTS datasets compared to SOTA methods. The code is available at https://github.com/Frank-Wang-oss/FCSTGNN.

Fully-Connected Spatial-Temporal Graph for Enhanced Multivariate Time-Series Analysis

Graphs have emerged as a powerful representation within machine learning domains for capturing structured relationships. This paper introduces a novel approach, Fully-Connected Spatial-Temporal Graph Neural Network (FC-STGNN), which addresses the intricate task of modeling Multivariate Time-Series (MTS) data by leveraging fully connected graph structures. This research departs from traditional Graph Neural Networks (GNNs) that often treat spatial and temporal dependencies in isolation. Instead, the authors propose a comprehensive model that integrates Spatial-Temporal (ST) dependencies, particularly focusing on the overlooked correlations between Different sEnsors at Different Timestamps (DEDT).

Proposed Methodology

FC-STGNN is built upon two key components: FC graph construction and FC graph convolution.

1. FC Graph Construction: This method employs a fully connected graph approach, where sensors across all timestamps are linked. The construction of this graph is influenced by the temporal distances between sensors, encoded in a decay graph, which ensures that sensors temporally closer exhibit stronger correlations. This innovative approach mitigates the limitations of existing GNN methods that fail to model ST dependencies comprehensively.
2. FC Graph Convolution: After graph construction, the paper introduces a moving-pooling GNN layer, which operates under a sliding window mechanism. This layer is responsible for capturing local spatial-temporal relationships, which are then aggregated through pooling to form high-level sensor features. The moving-pooling strategy effectively balances the global connectivity of the FC graph with localized temporal patterns.

The combination of these components allows FC-STGNN to maintain a dynamic spatio-temporal model adaptable to a range of MTS datasets.

Empirical Results

The paper reveals that FC-STGNN achieves enhanced performance across various MTS datasets, outperforming state-of-the-art methods in predictive tasks like equipment failure (RUL prediction) and human activity recognition (HAR). Notably, results from contextually diverse datasets such as C-MAPSS indicate reduced RMSE and improved Score metrics, highlighting the effectiveness of the proposed model in capturing intricate dependencies within MTS data. Furthermore, the performance gains on datasets like UCI-HAR, with accuracy improvements over existing methods, suggest the model’s robustness across applications and domains.

Implications and Future Directions

This research signifies an advancement in how spatio-temporal data can be modeled, suggesting broader applicability within AI contexts that necessitate complex dependency management. Ensuring full capture of DEDT may prove crucial in domains extending beyond time-series analysis, potentially influencing dynamic network analysis, predictive modeling in IoT environments, and autonomous decision systems.

Future developments in AI could build upon this framework, potentially exploring more sophisticated graph structures and convolutional layers that capitalize on the broader scope of dependencies. The authors’ code availability enhances reproducibility and fosters an environment for continued experimentation and refinement of graph-based models in time-series analysis.

In conclusion, FC-STGNN represents a significant contribution to the field of multivariate time-series analysis by effectively modeling comprehensive ST dependencies. The method’s design and empirical successes furnish a promising pathway for future AI systems that face the complexity of spatio-temporal data integration.