UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

• URL: http://arxiv.org/abs/2604.16325v1
• Date: Fri, 06 Mar 2026 05:00:28 GMT
• Title: UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
• Abstract summary: UniMamba is a unified spatial-temporal forecasting framework.<n>It integrates efficient state-space dynamics with attention-based dependency learning.<n>It consistently outperforms state-of-the-art forecasting models in both forecasting accuracy and computational efficiency.
• Score: 24.102050375727263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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