In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of model evaluations. We argue that the lookback window must be tuned on a per-task basis to ensure fair comparisons. Our empirical results show that failing to do so can invert performance rankings, particularly when comparing univariate and multivariate methods. Experiments on standard benchmarks reposition Channel-Independent (CI) models, such as PatchTST, as state-of-the-art methods. However, we reveal this superior performance is largely an artifact of weak inter-channel correlations and simplicity of patterns within these specific datasets. Using Granger causality analysis and ODE datasets (with implicit channel correlations), we demonstrate that the true strength of multivariate Channel-Dependent (CD) models emerges on datasets with strong, inherent cross-channel dependencies, where they significantly outperform CI models. We conclude with four key recommendations for improving TSF research: (i) consider the lookback window as a key hyperparameter to tune, (ii) for standard datasets, examining CI architectures is advantageous, (iii) leverage statistical analysis of datasets to guide the choice between CI and CD architectures, and (iv) prefer CD models in scenarios with limited data.

翻译：在长期时间序列预测（LTSF）中，回看窗口是一个关键的超参数，其设置往往具有随意性，从而损害了模型评估的有效性。我们认为，为确保公平比较，必须针对每个任务单独调整回看窗口。我们的实证结果表明，若不这样做，尤其是在比较单变量与多变量方法时，可能导致性能排名反转。在标准基准测试上的实验，将通道独立（CI）模型（如PatchTST）重新定位为最先进的方法。然而，我们发现这种优越性能在很大程度上是特定数据集中通道间相关性较弱以及模式简单性所导致的假象。通过使用格兰杰因果分析和ODE数据集（具有隐含的通道相关性），我们证明了多变量通道依赖（CD）模型的真正优势在于那些具有强固有跨通道依赖性的数据集上，它们在这些数据集上显著优于CI模型。最后，我们提出了改进TSF研究的四项关键建议：（i）将回看窗口视为需要调整的关键超参数；（ii）对于标准数据集，研究CI架构是有利的；（iii）利用数据集的统计分析来指导在CI与CD架构之间做出选择；（iv）在数据有限的场景中，优先考虑CD模型。