CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting
This work addresses a specific bottleneck in time series forecasting models for applications requiring cross-channel dependency capture, representing an incremental improvement over existing Mamba-based approaches.
The paper tackles the problem of inadequate cross-channel dependency handling in Mamba models for multivariate time series forecasting by introducing CMamba, which incorporates a modified Mamba module, a global data-dependent MLP, and a Channel Mixup mechanism, achieving improved forecasting performance as demonstrated on seven real-world datasets.
Recent advancements in multivariate time series forecasting have been propelled by Linear-based, Transformer-based, and Convolution-based models, with Transformer-based architectures gaining prominence for their efficacy in temporal and cross-channel mixing. More recently, Mamba, a state space model, has emerged with robust sequence and feature mixing capabilities. However, the suitability of the vanilla Mamba design for time series forecasting remains an open question, particularly due to its inadequate handling of cross-channel dependencies. Capturing cross-channel dependencies is critical in enhancing the performance of multivariate time series prediction. Recent findings show that self-attention excels in capturing cross-channel dependencies, whereas other simpler mechanisms, such as MLP, may degrade model performance. This is counterintuitive, as MLP, being a learnable architecture, should theoretically capture both correlations and irrelevances, potentially leading to neutral or improved performance. Diving into the self-attention mechanism, we attribute the observed degradation in MLP performance to its lack of data dependence and global receptive field, which result in MLP's lack of generalization ability. Based on the above insights, we introduce a refined Mamba variant tailored for time series forecasting. Our proposed model, \textbf{CMamba}, incorporates a modified Mamba (M-Mamba) module for temporal dependencies modeling, a global data-dependent MLP (GDD-MLP) to effectively capture cross-channel dependencies, and a Channel Mixup mechanism to mitigate overfitting. Comprehensive experiments conducted on seven real-world datasets demonstrate the efficacy of our model in improving forecasting performance.