Long-term time series forecasting has gained significant attention in recent years. While there are various specialized designs for capturing temporal dependency, previous studies have demonstrated that a single linear layer can achieve competitive forecasting performance compared to other complex architectures. In this paper, we thoroughly investigate the intrinsic effectiveness of recent approaches and make three key observations: 1) linear mapping is critical to prior long-term time series forecasting efforts; 2) RevIN (reversible normalization) and CI (Channel Independent) play a vital role in improving overall forecasting performance; and 3) linear mapping can effectively capture periodic features in time series and has robustness for different periods across channels when increasing the input horizon. We provide theoretical and experimental explanations to support our findings and also discuss the limitations and future works. Our framework's code is available at \url{https://github.com/plumprc/RTSF}.

翻译：长期时间序列预测近年来受到了广泛关注。尽管存在各种专门用于捕获时间依赖性的设计，但先前研究表明，与复杂架构相比，单一线性层即可取得具有竞争力的预测性能。本文深入探究了近期方法的内在有效性，并提出了三个关键观察：1) 线性映射对先前的长期时间序列预测工作至关重要；2) RevIN（可逆归一化）和CI（通道独立）在提升整体预测性能中发挥了关键作用；3) 线性映射能够有效捕获时间序列中的周期性特征，并且在增加输入时间跨度时，对不同通道的周期具有鲁棒性。我们提供了理论和实验解释以支撑研究发现，并讨论了局限性与未来工作。我们的框架代码见 \url{https://github.com/plumprc/RTSF}。