  In multivariate time-series forecasting (MTSF), extracting the temporal
correlations of the input sequences is crucial. While popular Transformer-based
predictive models can perform well, their quadratic computational complexity
results in inefficiency and high overhead. The recently emerged Mamba, a
selective state space model, has shown promising results in many fields due to
its strong temporal feature extraction capabilities and linear computational
complexity. However, due to the unilateral nature of Mamba, channel-independent
predictive models based on Mamba cannot attend to the relationships among all
variables in the manner of Transformer-based models. To address this issue, we
combine fast-attention with Mamba to introduce a novel framework named FMamba
for MTSF. Technically, we first extract the temporal features of the input
variables through an embedding layer, then compute the dependencies among input
variables via the fast-attention module. Subsequently, we use Mamba to
selectively deal with the input features and further extract the temporal
dependencies of the variables through the multi-layer perceptron block
(MLP-block). Finally, FMamba obtains the predictive results through the
projector, a linear layer. Experimental results on eight public datasets
demonstrate that FMamba can achieve state-of-the-art performance while
maintaining low computational overhead.
