reviews: 
Comments: This paper attempts to use the convolutional neural network (CNN) and proposes WinNet for long-term time series forecasting, which is different from most existing works based on Transformer or MLP. Specifically, WinNet first transforms the 1D time series to 2D tensor according to the predefined periodic windows and then performs period-trend and oscillation decomposition. After that, WinNet captures the correlation between period-trend and oscillation terms, based on which convolution and MLP layers are used to get the final prediction. Nine benchmark datasets are used to evaluate the proposed WinNet compared with some baseline methods.
Strengths: This paper attempts to use the convolutional neural network (CNN) for a better long-term time series prediction, which is still seldomly considered.
Weaknesses: 1. The blocks in the architecture of WinNet are not introduced clearly in Section 3. e.g.,

 -The I2PE block: How to train the MLP; How to calculate the periodic window when using two periods approximately as one period (11/12 or 23/24) in Table 1; why use n = w; Should we do padding for X_1D before performing Equation (1); What is the detailed process of I2PE in Equation (1) (I can guess the details but they should be described clearly)?

 -The TDPD block: what is the meaning of w×w in Equation (2), is it the kernel_size for AvgPool2d; What are the two inputs in "According to the equation 2, the two inputs can be decomposed into the period-trend and oscillation terms"?

 -The DCB block: What is the process of CI (channel independence strategy) in Equation (3)?

 -The Series Decoder block: How to get  X_{i}^{row } and X_{w ·⌈i/w⌉−(w−1)+(i mod w)}^{col} from X_{output}^{CI} by inter-period convolution and intra-period convolution; What are the details of these two kinds of convolutions; What is the meaning of {w ·⌈i/w⌉−(w−1)+(i mod w)}?

 -Figure 1: What is the process of CA.

2. The proposal WinNet does not compare with SoTA methods and the performance improvement is not significant.

 -It is said in the Appendix that some experimental results are taken from the PatchTST and PETformer, but there is no comparison with PETformer. There are also other MLP-based models (RLinear and RMLP), which outperform PatchTST on some datasets and should be compared with.

   Li, Zhe, et al. "Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping." arXiv preprint arXiv:2305.10721 (2023).

 -WinNet cannot beat existing models in many cases even based on the results shown in the manuscript.

 -Why the results of the exchange dataset are not given in Table 2?

 -The architectures for the Ablation Studies are not described clearly. In addition, how about the results by using only inter-period or intra-period branch in Figure 1?

 -Which kinds of Time and Memory are considered in Table 6?

3. Some claims are not clear, e.g., it is not clear why "The correlation between period-trend and oscillation terms can provide the local periodicity in time series".

4. The code is not available for reproducibility.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 2 fair