| Title |
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction |
| ID_Doc |
41666 |
| Authors |
Zhang, ZJ; Zhao, XY; Liu, QD; Zhang, CX; Ma, Q; Wang, WY; Zhao, HW; Wang, YQ; Liu, ZT |
| Title |
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction |
| Year |
2023 |
| Published |
|
| DOI |
10.1145/3583780.3615016 |
| Abstract |
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 howto 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. |
| Author Keywords |
smart city; spatio-temporal prediction; multi-attribute prediction; prompt learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001161549503025 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems |
| Research Area |
Computer Science |
| PDF |
https://arxiv.org/pdf/2309.09500
|