<p>The field of time series forecasting has been seen widespread application of Transformer-based architectures. However, the quadratic complexity of the attention mechanism limits its performance in long-term time series forecasting. The proposition of patching mechanism has alleviated this issue to some extent, but models will struggle to effectively unify the information between intra-patch and inter-patch. To address this problem, we propose DualMamba, a novel Mamba-based model for time series forecasting, which segments the time series into subseries-level patches and employs dual Mamba modules to capture local and global information separately. Specifically, the time series use patch-wise dependencies to guide the local module, where each patch uses a point-wise representation of time series data. Furthermore, we design an information fusion mechanism for integrating information between intra-patch and inter-patch, which effectively incorporates global information into local contexts. This allows the model to capture both local details and global trends. Extensive experiments on several real-world datasets demonstrate that DualMamba achieves state-of-the-art performance in most cases and has reliable robustness, making it highly adaptable for various types of time series.</p>