Proceedings of the AAAI Conference on Artificial Intelligence· 2020
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
- 1,509citations
- 2020year
Short summary
A new model, STSGCN, effectively forecasts spatial-temporal network data by simultaneously capturing localized spatial and temporal correlations and handling data heterogeneities across different time periods.
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Key points
- Proposes STSGCN, a novel model for spatial-temporal network data forecasting.
- Features a synchronous modeling mechanism to capture localized spatial-temporal correlations.
- Includes multiple modules to effectively handle heterogeneities in data across different time periods.
- Achieves state-of-the-art performance on four real-world datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.
The authors' abstract, as published at the source. Proceedings of the AAAI Conference on Artificial Intelligence, 2020 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering