PatchMamba: Multivariate Time Series Forecasting Model Based on Patch Attention and Mamba

Multivariate time series forecasting MTSF has broad applications in real-life situations. However, multivariate time series data in different scenarios may exhibit different inter-sequence or intra-sequence dependencies. Existing models often struggle to capture these complex dependencies between time series and variables simultaneously, thus limiting forecasting accuracy. To address these issues, this paper proposes PatchMamba, a multivariate time series forecast model based on Patch Attention and Mamba. PatchMamba includes two novel Patch Attention mechanisms: CD-Patch Attention under the channel dependence strategy and CI-Patch Attention under the channel independence strategy. CD-Patch effectively captures the inter-variable dependencies. In contrast, CI-Patch Attention takes each variable individually to extract local features, avoiding cross-channel interference. Furthermore, we use bidirectional Mamba Bi-Mamba to capture long temporal dependency information. Experiments show that PatchMamba achieves higher forecast accuracy on multiple real-world datasets compared to current state-of-the-art SOTA models. In addition, this paper validates the role and robustness of the model components through ablation experiments and parameter sensitivity analysis.