DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting

• URL: http://arxiv.org/abs/2504.01531v1
• Date: Wed, 02 Apr 2025 09:18:43 GMT
• Title: DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
• Authors: Xiaobei Zou, Luolin Xiong, Kexuan Zhang, Cesare Alippi, Yang Tang,
• Abstract summary: We propose a Relation and Adaptive Network Distribution (DRAN) capable of adapting to and distribution changes over time.<n>We show that our SFL efficiently preserves spatial relationships across various temporal operations.<n>Our approach outperforms state of the art methods in weather prediction and traffic forecasting flows.
• Score: 19.064628208136273
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate predictions of spatio-temporal systems' states are crucial for tasks such as system management, control, and crisis prevention. However, the inherent time variance of spatio-temporal systems poses challenges to achieving accurate predictions whenever stationarity is not granted. To address non-stationarity frameworks, we propose a Distribution and Relation Adaptive Network (DRAN) capable of dynamically adapting to relation and distribution changes over time. While temporal normalization and de-normalization are frequently used techniques to adapt to distribution shifts, this operation is not suitable for the spatio-temporal context as temporal normalization scales the time series of nodes and possibly disrupts the spatial relations among nodes. In order to address this problem, we develop a Spatial Factor Learner (SFL) module that enables the normalization and de-normalization process in spatio-temporal systems. To adapt to dynamic changes in spatial relationships among sensors, we propose a Dynamic-Static Fusion Learner (DSFL) module that effectively integrates features learned from both dynamic and static relations through an adaptive fusion ratio mechanism. Furthermore, we introduce a Stochastic Learner to capture the noisy components of spatio-temporal representations. Our approach outperforms state of the art methods in weather prediction and traffic flows forecasting tasks. Experimental results show that our SFL efficiently preserves spatial relationships across various temporal normalization operations. Visualizations of the learned dynamic and static relations demonstrate that DSFL can capture both local and distant relationships between nodes. Moreover, ablation studies confirm the effectiveness of each component.

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