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发表刊物：Frontiers of Computer Science

关键字：Long-term time series forecasting, State Space Model, Mamba, Patching

摘要：The field of time series forecasting has
been seen widespread application of Transformerbased 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 intrapatch 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 designed a information fusion mechanism for integrating information between intra-patch and interpatch, 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-theart performance in most cases and has reliable robustness, making it highly adaptable for various
types of time series.

是否译文：否

收录刊物：SCI

通讯作者：冯爱民