PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction

by   Zijian Zhang, et al.

In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility.


page 1

page 2

page 3

page 4


Dynamic Graph Convolution Network with Spatio-Temporal Attention Fusion for Traffic Flow Prediction

Accurate and real-time traffic state prediction is of great practical im...

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

With the acceleration of urbanization, traffic forecasting has become an...

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

Spatio-Temporal prediction plays a critical role in smart city construct...

Spatio-Temporal Multi-step Prediction of Influenza Outbreaks

Flu circulates all over the world. The worldwide infection places a subs...

Mining Spatio-temporal Data on Industrialization from Historical Registries

Despite the growing availability of big data in many fields, historical ...

Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs

In this paper, we tackle the problem of spatio-temporal tagging of self-...

Event-Aware Multimodal Mobility Nowcasting

As a decisive part in the success of Mobility-as-a-Service (MaaS), spati...

Please sign up or login with your details

Forgot password? Click here to reset