Summary of Unist: a Prompt-empowered Universal Model For Urban Spatio-temporal Prediction, by Yuan Yuan et al.
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
by Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li
First submitted to arxiv on: 19 Feb 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 study introduces UniST, a universal model designed for general urban spatio-temporal prediction. Unlike existing approaches that require task-specific designs and extensive domain-specific training data, UniST leverages diverse spatio-temporal data from different scenarios to capture complex dynamics. Inspired by large language models, UniST achieves success through pre-training, knowledge-guided prompts, and effective utilization of various datasets. The model’s performance is evaluated on over 20 spatio-temporal scenarios, demonstrating state-of-the-art results in few-shot and zero-shot prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to predict what will happen in cities over time and space. Currently, different methods are used for different city scenarios. The researchers developed a single model that can work well across many scenarios, using data from various cities and times. This model, called UniST, is like a large language model but designed specifically for urban prediction. It performs very well on many different scenarios, even when it hasn’t seen the exact same situation before. |
Keywords
* Artificial intelligence * Few shot * Large language model * Zero shot
