Summary of St-fit: Inductive Spatial-temporal Forecasting with Limited Training Data, by Zhenyu Lei et al.
ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data
by Zhenyu Lei, Yushun Dong, Jundong Li, Chen Chen
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Spatial-Temporal Graph Neural Networks (STGNNs) are a powerful tool for extracting insights from spatial-temporal graphs, which are widely used in various real-world applications. However, most existing methods require training data that may not be available for all nodes due to the asynchronous nature of outbreaks or other phenomena. To address this issue, we propose ST-FiT, a principled framework that consists of temporal data augmentation and spatial graph topology learning components. These components enable STGNNs to generalize well onto nodes without any available temporal training data. Our approach can be used on top of existing STGNNs to achieve superior performance in multiple key perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary STGNNs are a type of AI that helps us understand patterns and trends in things like city outbreaks or weather forecasts. But sometimes, we don’t have enough information to train these models properly. In this paper, the authors introduce a new way to fix this problem by making STGNNs work even when there’s no training data available for some nodes. They call it ST-FiT and show that it can do better than other methods in many situations. |
Keywords
» Artificial intelligence » Data augmentation
