UniMamba Framework Integrates State-Space and Attention for Time Series Forecasting

A novel framework for spatial-temporal modeling, named UniMamba, has been unveiled to tackle issues in multivariate time series forecasting. This framework combines efficient state-space dynamics with attention-based learning of dependencies. It is designed for sectors such as energy, finance, and environmental monitoring, where intricate temporal dependencies and interactions among variables pose ongoing challenges. While existing Transformer-based approaches utilize attention mechanisms to capture temporal correlations, they incur quadratic computational costs. Conversely, state-space models like Mamba provide effective long-context modeling but lack clear temporal pattern recognition. UniMamba introduces a Mamba Variate-Channel Encoding Layer, augmented with FFT-Laplace Transform and TCN, to capture global temporal dependencies. Additionally, a Spatial Temporal Attention Layer models inter-variable correlations and temporal changes, while a Feedforward Temporal Dynamics Layer boosts the framework's functionality. This announcement was published on arXiv under the identifier arXiv:2604.16325v1 and is classified as a cross announcement type.