  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}.
