UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910217962258432 |
|---|---|
| author | Chen, Xingsheng Mu, Xianpei Yi, Deyu Yuan, Yilin He, Xingwei Gao, Bo Zhang, Regina Lio, Pietro Yiu, Siu-Ming |
| author_facet | Chen, Xingsheng Mu, Xianpei Yi, Deyu Yuan, Yilin He, Xingwei Gao, Bo Zhang, Regina Lio, Pietro Yiu, Siu-Ming |
| contents | 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 Encoding Layer enhanced with FFT-Laplace Transform and TCN to capture global temporal dependencies, and a Spatial Temporal Attention Layer to jointly model inter-variate correlations and temporal evolution. A Feedforward Temporal Dynamics Layer further fuses continuous and discrete contexts for accurate forecasting. Comprehensive experiments on eight public benchmark datasets demonstrate that UniMamba consistently outperforms state-of-the-art forecasting models in both forecasting accuracy and computational efficiency, establishing a scalable and robust solution for long-sequence multivariate time-series prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16325 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration Chen, Xingsheng Mu, Xianpei Yi, Deyu Yuan, Yilin He, Xingwei Gao, Bo Zhang, Regina Lio, Pietro Yiu, Siu-Ming Machine Learning Artificial Intelligence 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 Encoding Layer enhanced with FFT-Laplace Transform and TCN to capture global temporal dependencies, and a Spatial Temporal Attention Layer to jointly model inter-variate correlations and temporal evolution. A Feedforward Temporal Dynamics Layer further fuses continuous and discrete contexts for accurate forecasting. Comprehensive experiments on eight public benchmark datasets demonstrate that UniMamba consistently outperforms state-of-the-art forecasting models in both forecasting accuracy and computational efficiency, establishing a scalable and robust solution for long-sequence multivariate time-series prediction. |
| title | UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.16325 |