  In the realm of time series forecasting (TSF), it is imperative for models to
adeptly discern and distill hidden patterns within historical time series data
to forecast future states. Transformer-based models exhibit formidable efficacy
in TSF, primarily attributed to their advantage in apprehending these patterns.
However, the quadratic complexity of the Transformer leads to low computational
efficiency and high costs, which somewhat hinders the deployment of the TSF
model in real-world scenarios. Recently, Mamba, a selective state space model,
has gained traction due to its ability to process dependencies in sequences
while maintaining near-linear complexity. For TSF tasks, these characteristics
enable Mamba to comprehend hidden patterns as the Transformer and reduce
computational overhead compared to the Transformer. Therefore, we propose a
Mamba-based model named Simple-Mamba (S-Mamba) for TSF. Specifically, we
tokenize the time points of each variate autonomously via a linear layer. A
bidirectional Mamba layer is utilized to extract inter-variate correlations and
a Feed-Forward Network is set to learn temporal dependencies. Finally, the
generation of forecast outcomes through a linear mapping layer. Experiments on
thirteen public datasets prove that S-Mamba maintains low computational
overhead and achieves leading performance. Furthermore, we conduct extensive
experiments to explore Mamba's potential in TSF tasks. Our code is available at
https://github.com/wzhwzhwzh0921/S-D-Mamba.
