Urban spatio-temporal prediction is crucial for informed decision-making,
such as transportation management, resource optimization, and urban planning.
Although pretrained foundation models for natural languages have experienced
remarkable breakthroughs, wherein one general-purpose model can tackle multiple
tasks across various domains, urban spatio-temporal modeling lags behind.
Existing approaches for urban prediction are usually tailored for specific
spatio-temporal scenarios, requiring task-specific model designs and extensive
in-domain training data. In this work, we propose a universal model, UniST, for
urban spatio-temporal prediction. Drawing inspiration from large language
models, UniST achieves success through: (i) flexibility towards diverse
spatio-temporal data characteristics, (ii) effective generative pre-training
with elaborated masking strategies to capture complex spatio-temporal
relationships, (iii) spatio-temporal knowledge-guided prompts that align and
leverage intrinsic and shared knowledge across scenarios. These designs
together unlock the potential of a one-for-all model for spatio-temporal
prediction with powerful generalization capability. Extensive experiments on 15
cities and 6 domains demonstrate the universality of UniST in advancing
state-of-the-art prediction performance, especially in few-shot and zero-shot
scenarios.

UniST 是一个通用模型，通过对多样的时空数据特征的灵活性、精心设计的掩模策略以及时空知识引导的提示，实现对城市时空预测的强大泛化能力，15 个城市和 6 个领域的广泛实验证明了 UniST 在提升最先进预测性能方面的普适性，特别是在少样本和零样本情况下。

UniST：用于城市时空预测的提示增强通用模型

UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal  Prediction

The design of deep graph models still remains to be investigated and the
crucial part is how to explore and exploit the knowledge from different hops of
neighbors in an efficient way. In this paper, we propose a novel RNN-like deep
graph neural network architecture by incorporating AdaBoost into the
computation of network; and the proposed graph convolutional network called
AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently
extract knowledge from high-order neighbors of current nodes and then
integrates knowledge from different hops of neighbors into the network in an
Adaboost way. Different from other graph neural networks that directly stack
many graph convolution layers, AdaGCN shares the same base neural network
architecture among all ``layers'' and is recursively optimized, which is
similar to an RNN. Besides, We also theoretically established the connection
between AdaGCN and existing graph convolutional methods, presenting the
benefits of our proposal. Finally, extensive experiments demonstrate the
consistent state-of-the-art prediction performance on graphs across different
label rates and the computational advantage of our approach
AdaGCN~\footnote{Code is available at https://github.com/datake/AdaGCN.}

本文提出了一种融合了 AdaBoost 的新型 RNN-like 深度图形神经网络体系结构，该网络能够高效地从当前节点的高阶邻居中提取知识，并以 Adaboost 的方式将不同跳数邻居的知识集成到网络中。通过它与现有图卷积方法的理论联系，我们的 AdaGCN 能够在不同标签率下展现出一致的最先进的预测性能，而且计算效率有所提升。