SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting

Spiking Neural Networks (SNNs) offer a distinctive paradigm for temporal modeling through their intrinsic membrane potential dynamics. However, existing SNN-based forecasting methods focus exclusively on temporal processing, lacking mechanisms to capture spatial dependencies among variables. To bridge this gap, we propose SpikeSTAG, a neuromorphic spatiotemporal architecture that integrates graph-based spatial reasoning into spike-driven temporal computation. Central to our approach is the Dendritic Graph Module, which reinterprets spectral graph convolutions as biological dendritic integration -- coupling multi-scale spatial aggregation with the temporal dynamics of spiking neurons within a single computational unit. To adaptively fuse the resulting spatial and temporal representations, we further introduce a Dual-Stream Integration Module that employs competitive gating and coincidence-based amplification, selectively enhancing consistent spatiotemporal patterns while suppressing conflicting noise. Extensive experiments demonstrate that SpikeSTAG establishes a new state-of-the-art among SNN-based models and surpasses representative ANN baselines, while reducing theoretical energy consumption by approximately 33.3\% compared to Transformer architectures.