SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration

• URL: http://arxiv.org/abs/2508.02069v1
• Date: Mon, 04 Aug 2025 05:17:52 GMT
• Title: SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration
• Authors: Bang Hu, Changze Lv, Mingjie Li, Yunpeng Liu, Xiaoqing Zheng, Fengzhe Zhang, Wei cao, Fan Zhang,
• Abstract summary: Spiking neural networks (SNNs) offer a distinctive approach for capturing the complexities of temporal data.<n>We introduce a brand new SNN architecture, which is among the first to seamlessly integrate graph structural learning with spike-based temporal processing.<n> Experiments show that our model surpasses the state-of-the-art SNN-based iSpikformer on all datasets.
• Score: 16.754715227269525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, offer a distinctive approach for capturing the complexities of temporal data. However, their potential for spatial modeling in multivariate time-series forecasting remains largely unexplored. To bridge this gap, we introduce a brand new SNN architecture, which is among the first to seamlessly integrate graph structural learning with spike-based temporal processing for multivariate time-series forecasting. Specifically, we first embed time features and an adaptive matrix, eliminating the need for predefined graph structures. We then further learn sequence features through the Observation (OBS) Block. Building upon this, our Multi-Scale Spike Aggregation (MSSA) hierarchically aggregates neighborhood information through spiking SAGE layers, enabling multi-hop feature extraction while eliminating the need for floating-point operations. Finally, we propose a Dual-Path Spike Fusion (DSF) Block to integrate spatial graph features and temporal dynamics via a spike-gated mechanism, combining LSTM-processed sequences with spiking self-attention outputs, effectively improve the model accuracy of long sequence datasets. Experiments show that our model surpasses the state-of-the-art SNN-based iSpikformer on all datasets and outperforms traditional temporal models at long horizons, thereby establishing a new paradigm for efficient spatial-temporal modeling.

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