Summary of Ssmt: Few-shot Traffic Forecasting with Single Source Meta-transfer, by Kishor Kumar Bhaumik et al.
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer
by Kishor Kumar Bhaumik, Minha Kim, Fahim Faisal Niloy, Amin Ahsan Ali, Simon S. Woo
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes Single Source Meta-Transfer Learning (SSMT), a novel approach for few-shot traffic forecasting that relies only on a single source city’s data. This method is designed to overcome the limitations of collecting data from multiple cities, which can be costly and time-consuming. SSMT uses memory-augmented attention to store spatial knowledge from the source city and selectively recall it for the target city with limited data. The paper also introduces meta-learning tasks using sinusoidal positional encoding and meta-positional encoding to capture a more generalized representation of temporal patterns across all tasks. Experimental results on five real-world benchmark datasets show that SSMT outperforms existing methods in time series traffic prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic forecasting is crucial for intelligent transportation systems, but collecting data from multiple cities can be costly and time-consuming. A new approach called Single Source Meta-Transfer Learning (SSMT) uses only one city’s data to predict traffic patterns in another city with limited data. This method stores spatial knowledge from the source city and selectively recalls it for the target city. The result is a better way to forecast traffic, which can help cities make smarter decisions about transportation. |
Keywords
» Artificial intelligence » Attention » Few shot » Meta learning » Positional encoding » Recall » Time series » Transfer learning
