* The contribution of the paper seems limited. According to equation (10), the core approach mainly involves applying a linear transformation to frequency components based on the matrix $\mathrm{S}$. It is unclear how $\mathrm{S}$ functions as a constraint to realize the key motivation of enhancing the weight of stable frequency components.
* In Definition 2, the authors introduce the concept of a Stable Frequency Subset $\mathcal{O}$ but do not provide a clear criterion for determining what qualifies as a stable frequency. Some notations, such as Stable Frequency Subset $\mathcal{O}$, and Theorem 1 presented in Section 2 are not well linked to the design of the method proposed in Section 3.
* The experimental evaluation lacks thoroughness, as it includes only three baseline models (Dlinear, PatchTST, and iTransformer). Although the authors mention other models, such as CrossFormer, they do not include it in their comparisons. Additionally, the Nonstationary Transformer [1] would also be a relevant model for further comparison.
* The empirical analysis in Figure 3 is confusing to the reviewer. Could the authors provide additional clarification on how the adjustments to the amplitudes of the input series and forecasting target, based on the Frequency Stability score, affect model prediction accuracy? Additionally, could you explain why these adjustments are effective in enhancing the model's performance? Both Equations (9) and (10) have large spacing from the preceding text.
[1] Liu, Yong, et al. "Non-stationary transformers: Exploring the stationarity in time series forecasting." Advances in Neural Information Processing Systems 35 (2022): 9881-9893.
* The empirical analysis in Figure 3 is confusing to the reviewer. Could the authors provide additional clarification on how the adjustments to the amplitudes of the input series and forecasting target, based on the Frequency Stability score, affect model prediction accuracy? Additionally, could you explain why these adjustments are effective in enhancing the model's performance? Both Equations (9) and (10) have large spacing from the preceding text. [1] Liu, Yong, et al. "Non-stationary transformers: Exploring the stationarity in time series forecasting." Advances in Neural Information Processing Systems 35 (2022): 9881-9893.