---
title: "Time-Frequency Disentanglement Boosted Pre-Training: A Universal Spatio-Temporal Modeling Framework"
authors:
  - "Yudong Zhang"
  - "Zhaoyang Sun"
  - "Xu Wang"
  - "Xuan Yu"
  - "Kai Wang"
  - "Yang Wang"
doi: "10.24963/ijcai.2025/407"
url: "https://ijcai.org/proceedings/2025/407"
pdf: "https://ijcai.org/proceedings/2025/0407.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "3660-3669"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining spatial and/or temporal data"
  - "Multidisciplinary Topics and Applications: MTA: Transportation"
  - "Machine Learning: ML: Time series and data streams"
---

# Time-Frequency Disentanglement Boosted Pre-Training: A Universal Spatio-Temporal Modeling Framework

Yudong Zhang, Zhaoyang Sun, Xu Wang, Xuan Yu, Kai Wang, Yang Wang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 3660-3669. DOI: [10.24963/ijcai.2025/407](https://doi.org/10.24963/ijcai.2025/407)

## Abstract

Current spatio-temporal modeling techniques largely rely on the abundant data and the design of task-specific models. However, many cities lack well-established digital infrastructures, making data scarcity and the high cost of model development significant barriers to application deployment. Therefore, this work aims to enable spatio-temporal learning to cope with the problems of few-shot data modeling and model generalizability. To this end, we propose a Universal Spatio-Temporal Correlationship pre-training framework (USTC), for spatio-temporal modeling across different cities and tasks. To enhance the spatio-temporal representations during pre-training, we propose to decouple the time-frequency patterns within data, and leverage contrastive learning to maintain the time-frequency consistency. To further improve the adaptability to downstream tasks, we design a prompt generation module to mine personalized spatio-temporal patterns on the target city, which can be integrated with the learned common spatio-temporal representations to collaboratively serve downstream tasks. Extensive experiments conducted on real-world datasets demonstrate that USTC significantly outperforms the advanced baselines in forecasting, imputation, and extrapolation across cities.