UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal dependencies and cross-variable interactions pose enduring challenges. Existing Transformer-based methods capture temporal correlations through attention mechanisms but suffer from quadratic computational cost, while state-space models like Mamba achieve efficient long-context modeling yet lack explicit temporal pattern recognition. Therefore we introduce UniMamba, a unified spatial-temporal forecasting framework that integrates efficient state-space dynamics with attention-based dependency learning. UniMamba employs a Mamba Variate-Channel ...
| Authors | Xingsheng Chen, Xianpei Mu, Deyu Yi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 27 June 2026 |
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