reviews: 
Comments: The paper introduces WinNet, a CNN-based model tailored for long-term time series forecasting. Traditional Transformer-based approaches, despite their advancements, struggle with computational efficiency and capturing the periodicity of time series data. WinNet seeks to address these issues with its unique architecture:

1. **Inter-Intra Period Encoder (I2PE):** Converts a 1D sequence into a 2D tensor, capturing both long and short periodicities.
2. **Two-Dimensional Period Decomposition (TDPD):** Models period-trend and oscillation terms, emphasizing the significance of periodicity in time series data.
3. **Decomposition Correlation Block (DCB):** Exploits the correlations between period-trend and oscillation terms, enhancing prediction capabilities.

A pivotal concept introduced is the "periodic window," derived as the least common multiple of multiple periods obtained via Fourier Frequency Transformation. This enables the model to represent variations of multiple short periods and organizes the sequence into a 2D tensor, wherein each row signifies a short-period trend and each column stands for the long-period trend.

WinNet's innovations result in a simplified structure with a single convolutional layer at its core, which significantly reduces computational complexity. Moreover, this model outperforms various baselines in both univariate and multivariate prediction tasks across multiple domains, as evidenced by experiments conducted on nine benchmark datasets.
Strengths: **Strengths:**

1. **Originality:** 
    - *Periodic Window Concept:* The inception of the periodic window, which is ascertained by the least common multiple of various periods using Fourier Frequency Transformation, offers a groundbreaking method to encapsulate the core characteristics of time series data.

2. **Quality:**
    - *Ease of Implementation:* WinNet is notably straightforward to implement, and empirical results demonstrate that it surpasses many other methods in a majority of scenarios.

3. **Efficiency:** 
    - *Optimized Parameters and Complexity:* WinNet has fewer parameters and a reduced computational complexity compared to MLP techniques and other prevailing methods.

4. **Clarity:**
    - *Well-crafted Manuscript:* The paper is articulately penned and provides a seamless reading experience, making it easy for readers to follow and comprehend.
Weaknesses: **Weaknesses:**

1. **Inconsistent Mathematical Notation:** 
    - The authors' presentation of mathematical symbols lacks consistency. As an example, function names should conventionally be displayed in regular typeface instead of italic. Furthermore, vectors and matrices should be represented in bold. Adhering to proper notation is pivotal for ensuring clarity and averting potential misunderstandings.

2. **Graphics and Formatting Issues:** 
    - The image quality in Figure 4 is noticeably poor. Additionally, the authors did not adhere to ICLR's guidelines, as they combined the appendix and main text, which could hamper structured reading and comprehension.

3. **On CNN-based MTS:**
    - Given the inherent characteristics of time series data, it's a general understanding that Transformer-based methods often underperform compared to Linear and CNN approaches in various scenarios. However, one of the advantages of Transformer methods is their ability to capture interrelationships amongst multi-variables. Evaluating CNN-based methods purely on accuracy might not provide a fair comparison. Moreover, several existing methods, such as Seq-VAE, leverage CNN for MTS tasks. A comparison of WinNet with such methods would have added depth to the evaluation.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 2 fair