STTP: Urban Spatio-Temporal Forecasting with Timestamp Information Mining and Pattern Differentiation

Chutong Ding, Aoyu Liu, Yaying Zhang

Abstract: Urban spatio-temporal prediction plays a crucial role in modern urban planning and management. However, spatio-temporal data exhibit complex and dynamic characteristics, presenting significant challenges for accurate forecasting. Although current state-of-the-art methods have made improvements, they still face two key limitations: (1) Many studies coarsely treat timestamps, failing to exploit the rich information embedded in timestamp data fully. (2) Most approaches overlook the differentiation of spatio-temporal patterns across urban nodes. Due to variations in the attributes of these nodes, even when local historical patterns are similar, future performance may differ. Thus, effective differentiation of spatio-temporal patterns is essential for accurate predictions. To address these challenges, we propose the STTP for urban spatio-temporal forecasting. Our approach introduces a trainable timestamp network to extract temporal correlation features from timestamps, using them as conditional prompts through an adaptive normalization technique to optimize spatio-temporal correlation capture. Additionally, we design a spatial attention module that differentiates node patterns, clustering similar patterns and differentiating dissimilar ones via cross-pattern and intra-pattern attention mechanisms. Extensive experimental results on six real-world datasets demonstrate that the STTP significantly outperforms existing state-of-the-art baselines. Codes are available at https://github.com/Dingct/STTP.

External IDs:dblp:conf/ijcnn/DingLZ25