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：长期时间序列预测领域涌现出多种模型。近期研究表明，采用通道依赖（CD）或通道独立（CI）建模的单一线性层，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。此外，CD和CI均为静态策略，需通过大量实验才能确定其对特定数据集的适用性，这也是极具挑战性的问题。本文重新审视了当前CI策略是否为时间序列预测的最佳解决方案。首先，我们针对线性模型提出一种简单有效的策略——CSC（$\mathbf{C}$hannel $\mathbf{S}$elf-$\mathbf{C}$lustering，通道自聚类）。其中，通道自聚类（CSC）在提升CI策略性能的同时减少参数量（例如在电力数据集上降低超过10倍），并显著缩短训练时间。其次，受自聚类思想启发，我们进一步提出适用于深度模型的通道重排（CR）方法，该方法的性能与基线模型相当。最后，我们探讨了使用同一通道历史值作为输入来预测未来值是否为最优方案。期待我们的发现与方法能为超越CD/CI的新方案提供启示。