Multivariate time series forecasting (TSF) is a fundamental task in intelligent systems, yet accurate and efficient modeling remains challenging under high dimensionality, non-stationarity, and complex cross-variate dependencies. The Mamba architecture provides an efficient linear-time backbone, but it still suffers from a multivariate representational bottleneck caused by unified state modeling. To address this limitation, we propose TSC-Mamba, a Mamba-centered framework built on a “Decoupling and Specialization” paradigm and organized as a cohesive “Decompose–Propagate–Correlate” pipeline. Specifically, the Adaptive Decomposition Fusion Module separates predictable low-frequency trends from high-frequency residual dynamics, while the Channel Interaction Fusion Module explicitly models structured cross-variate dependencies through an efficient low-rank mechanism. Experiments on eight public benchmark datasets show that TSC-Mamba achieves an average error reduction of up to 3.5% over the direct baseline S-Mamba while strictly maintaining linear complexity. Ablation studies validate the effectiveness of both modules, and Wilcoxon signed-rank analysis further confirms that the gains over S-Mamba are statistically significant. Additional experiments indicate strong run-to-run stability, robustness to input-length variation, improved generalization under partially visible variates, and more concentrated empirical predictive bands than S-Mamba. These results show that structured responsibility allocation is an effective strategy for enhancing state-space models in multivariate TSF.
Zhao et al. (Sat,) studied this question.