Time series forecasting is a practical cornerstone for decision-making in diverse fields including finance, energy management and meteorology, yet conventional Transformer-based models are plagued by high computational complexity inherent in their attention mechanism, as well as poor efficiency in capturing long-range dependencies when processing long-sequence time series data. Meanwhile, the recently proposed Mamba model built on a linear-complexity state space model has achieved strong performance in long-sequence modeling tasks, but the vanilla Mamba model still has obvious limitations in time series forecasting scenarios, including insufficient discriminability of channel features, unsatisfactory prediction accuracy, and information loss during feature transmission in the encoder layer. To address these issues, this paper proposes An Enhanced Mamba Integrating Dynamic Compression Ratio Channel Attention and Progressive Projection for Multivariate Time Series Forecasting, which incorporates three core improvement strategies: a lightweight dynamic compression ratio channel attention, a residual-optimized encoder, and a progressive prediction head. These strategies realize adaptive weighting of channel features, lossless information transmission in the encoder and gradual refinement of forecasting results respectively. Extensive experiments on multiple public time series datasets show that compared with the vanilla Mamba model and mainstream Transformer-based forecasting methods, the proposed model achieves significant improvements in prediction accuracy, along with stronger robustness and higher practical application value.