  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.
