Summary of Spatial-temporal Forecasting For Regions Without Observations, by Xinyu Su and Jianzhong Qi and Egemen Tanin and Yanchuan Chang and Majid Sarvi
Spatial-temporal Forecasting for Regions without Observations
by Xinyu Su, Jianzhong Qi, Egemen Tanin, Yanchuan Chang, Majid Sarvi
First submitted to arxiv on: 19 Jan 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 This paper proposes a novel spatial-temporal forecasting model, STSM, that can accurately predict traffic and air pollutant levels in regions without historical data. The authors address the issue of incomplete data by taking a contrastive learning-based approach to learn from adjacent regions with recorded data. They develop a selective masking strategy to enable learning and demonstrate the effectiveness of their method through experiments on traffic and air pollutant forecasting tasks. STSM outperforms adapted state-of-the-art models, reducing errors consistently in both domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to predict traffic and air quality levels even when there’s no data available from that specific area before. They wanted to find a solution for situations where sensors are not yet installed or open data is not available. To do this, they created a model called STSM that looks at patterns in neighboring areas with similar conditions. This approach allowed them to make more accurate predictions without needing historical data. |
