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：摘要：长期时间序列预测领域涌现了多种模型。近期研究表明，采用通道依赖（CD）或通道独立（CI）建模的单层线性模型，其性能甚至可超越大量复杂模型。然而，当前研究主要将CD和CI视为互补但互斥的两种方法，无法同时利用这两个极端策略。此外，CD和CI均属于静态策略，若无大量实验验证，无法确定其对特定数据集是否最优，这也是一个具有挑战性的问题。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，我们提出一种名为CSC（$\mathbf{C}$hannel $\mathbf{S}$elf-$\mathbf{C}$lustering，通道自聚类策略）的简单有效策略，适用于线性模型。通道自聚类（CSC）在提升CI策略性能的同时减少参数规模——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，我们进一步提出通道重排（CR）方法，这是一种受自聚类启发的深度模型方法。CR在基线对比中展现了具有竞争力的性能。最后，我们还讨论了是否应使用同一通道的历史数据预测其未来值。我们希望这些发现与方法能够启发超越CD/CI的新解决方案。