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

Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping