CAAT: Channel-Aggregated Attention Transformer for Efficient Multivariate Time Series Forecasting

Multivariate Time Series Forecasting MTSF holds significant application value in finance, energy, transportation, and other domains. Existing Transformer-based approaches typically face two critical challenges: 1 The computational complexity of cross-channel modeling grows quadratically with the number of channels, and 2 Noisy interference in cross-channel information leads to inefficient dependency modeling. This paper proposes a lightweight model named CAAT Channel-Aggregated Attention Transformer that achieves efficient forecasting through a Channel-Aggregation Module CAM and spatiotemporally decoupled attention mechanisms.The CAAT framework first compresses multivariate sequences into latent representations via MLPs, followed by saliency-based probabilistic sampling to select high signal-to-noise ratio channel features. Subsequently, the aggregated channel features are injected into the temporal dimension, enabling joint modeling of cross-temporal and cross-channel dependencies through temporal-axis attention mechanisms alone. Experimental results demonstrate that CAAT achieves significant improvements in prediction accuracy compared to baseline methods.