Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting performance by treating different channels individually, while it leads to poor generalization on unseen instances and ignores potentially necessary interactions between channels. Conversely, the Channel-Dependent (CD) strategy mixes all channels with even irrelevant and indiscriminate information, which, however, results in oversmoothing issues and limits forecasting accuracy. There is a lack of channel strategy that effectively balances individual channel treatment for improved forecasting performance without overlooking essential interactions between channels. Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM). CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster information instead of individual channel identities, combining the best of CD and CI worlds. Extensive experiments on real-world datasets demonstrate that CCM can (1) boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively; (2) enable zero-shot forecasting with mainstream time series forecasting models; (3) uncover intrinsic time series patterns among channels and improve interpretability of complex time series models.

翻译：时间序列预测在近几十年来受到了广泛关注。先前研究表明，通道独立（CI）策略通过单独处理不同通道提升了预测性能，但其在未见实例上泛化能力较差，且忽略了通道间潜在的必要交互。相反，通道依赖（CD）策略混合了所有通道信息（甚至包含不相关且无差别的信息），这会导致过度平滑问题并限制预测精度。目前尚缺乏一种能有效平衡个体通道处理（以提升预测性能）且不忽视通道间必要交互的通道策略。基于我们观察到的模型性能提升与通道对间内在相似性的关联现象，我们开发了一种新颖且自适应的通道聚类模块（CCM）。CCM能够动态聚合具有内在相似性的通道，并利用聚类信息而非单个通道标识，从而融合CD与CI策略的优势。在真实数据集上的大量实验表明，CCM能够：（1）在长短期预测任务中，分别将CI与CD模型的平均性能提升2.4%和7.2%；（2）实现主流时间序列预测模型的零样本预测；（3）揭示通道间的内在时间序列模式，并提升复杂时间序列模型的可解释性。