Reprogamming Cross-Channel Interaction for Noise-Resilient Time Series Forecasting

Transformer-based models have achieved remarkable performance in multivariate time series forecasting, benefiting from the attention mechanism. However, current Transformer-based forecasting paradigms still suffer from channel noise to the point that even perturbations from a single channel can significantly degrade overall performance. This vulnerability arises from three factors: noise propagation caused by token-to-token attention (*mechanism level*), loss of global information (*representation level*), and the lack of cross-channel interaction regularization (*learning level*). To address these challenges, we propose three innovations: *(i)* reprogramming attention into an MLP-based 'Contextual-weighted Interaction Block' (CoIn), *(ii)* introducing a learnable 'Global Token' to preserve global information, and *(iii)* employing 'Channel Decoupling Loss' to regularize cross-channel interaction. In particular, we renovate the traditional attention, which inevitably causes noise propagation between channels, into CoIn, which suppresses channel noise by reprogramming information flow in attention. A learnable Global Token is employed to aggregate and redistribute global information. We also introduce 'Channel Decoupling Loss', which regularizes cross-channel interactions during the learning process. Experiments on benchmark datasets show that these tools improve robustness to channel noise and outperform Transformer-based models in forecasting accuracy and efficiency. Code is available at this [**Anonymous Repo**](https://anonymous.4open.science/r/ResNo-laoda24).