There has been an emergence of various models for long-term time series forecasting. Recent studies have demonstrated that a single linear layer, using Channel Dependent (CD) or Channel Independent (CI) modeling, can even outperform a large number of sophisticated models. However, current research primarily considers CD and CI as two complementary yet mutually exclusive approaches, unable to harness these two extremes simultaneously. And it is also a challenging issue that both CD and CI are static strategies that cannot be determined to be optimal for a specific dataset without extensive experiments. In this paper, we reconsider whether the current CI strategy is the best solution for time series forecasting. First, we propose a simple yet effective strategy called CSC, which stands for $\mathbf{C}$hannel $\mathbf{S}$elf-$\mathbf{C}$lustering strategy, for linear models. Our Channel Self-Clustering (CSC) enhances CI strategy's performance improvements while reducing parameter size, for exmpale by over 10 times on electricity dataset, and significantly cutting training time. Second, we further propose Channel Rearrangement (CR), a method for deep models inspired by the self-clustering. CR attains competitive performance against baselines. Finally, we also discuss whether it is best to forecast the future values using the historical values of the same channel as inputs. We hope our findings and methods could inspire new solutions beyond CD/CI.

翻译：[translated abstract in Chinese] 近年来，针对长期时间序列预测涌现出多种模型。最新研究表明，采用通道依赖（CD）或通道独立（CI）建模的单一线性层，其性能甚至能超越大量复杂模型。然而，当前研究主要将CD和CI视为互补但互斥的两种方法，无法同时利用这两种极端策略。同时，一个棘手的问题在于：CD与CI均为静态策略，若不经过大量实验，无法确定其对特定数据集是否为最优选择。本文重新审视了当前CI策略是否是时间序列预测的最佳方案。首先，针对线性模型提出了一种简单而有效的策略——通道自聚类策略（CSC）。该通道自聚类方法（CSC）在提升CI策略性能的同时减少了参数规模（例如在电力数据集上参数减少超过10倍），并显著缩短训练时间。其次，受自聚类启发，我们进一步提出适用于深度模型的通道重排方法（CR），该方法在与基线对比时展现出竞争性性能。最后，本文讨论了使用同一通道的历史值预测未来值是否具有最优性。我们希望所提出的发现与方法能够启发超越CD/CI的新解决方案。