Kati8/ICLR_2023_2025 huggingface dataset:
authors: Wenjie Ou
authors: Dongyue Guo
authors: Zheng Zhang
authors: Zhishuo Zhao
authors: Yi Lin
reviews: 
Comments: In this paper, the authors aim to solve the problems of high computational costs and the missing periodic data capture in forecasting models. To achieve the goal, the authors designed a CNN-based model, WINNET, with one convolutional layer as the backbone. The model includes four parts, Inter-Intra Period Encoder (I2PE), Two-Dimensional Period Decomposition (TDPD), Decomposition Correlation Block (DCB) and Series Decoder. Specifically, I2PE transforms the input 1D sequence into 2D tensor with inter-period and intra-period. TDPD is to obtain the period-trend and oscillation terms. DCB is to study the correlation between the period-trend and oscillation terms. And finally, through Series Decoder, the final prediction results are obtained.
In the experiment, the authors evaluate the performance over the real-world datasets both small and large datasets, comparing to several baselines.
Strengths: Originality:
In this paper, the backbone of the model is mainly a convolution layer, which greatly reduces the computational complexity and improve the efficiency. It is novel and interesting for simplify model structure for time series forecasting tasks. And this try also shows that simple model framework could also effectively perform time series forecasting tasks.
Quality:
From the perspective of quality, it is high. The authors design a new model and demonstrate its effectiveness through detailed explanations. And the data analysis is thorough, well-executed, and adequately supports the conclusions drawn.
Clarity:
In this paper, the introduction provides a clear overview of the research topic and objectives, and the body sections are logically organized. And the language used in this paper is clear and easy to understand. Besides, some key concepts are well explained.
Significance:
The work in this paper is of great significance. Firstly, WINNET outperforms other forecasting models. And then, WINNET harvests the high computational efficiency for other forecasting models and make full use of the correlation between period trend and oscillation.
Weaknesses: (1) In figure 1, through I2PE block, you can get inter-period and intra-period features. The inter-period features represent the long-period features and the intra-period features represent the short-period features. However, in figure 1, you wrote that the short-period features are inter-period features, and the long-period features are intra-period features.
(2) In section 3.1, the framework of I2PE is needed.
(3) In section 3.4, the framework of series decoder is needed.
(4) In the experimental part, I noticed that for some data sets, WINNET's performance is not the best, not even the second best. Some explanation is needed.
(5) Please pay attention to typography issues, such as the size of Table 3. Table 4.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some 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
reviews: 
Comments: This work proposed a simple 2D-CNN framework for time series forecasting tasks, which mainly utilizes the multiscale periodic bias and achieves good forecasting accuracy with computational efficiency.
Strengths: The numerical results indicate a strong performance when compared to TimesNet, and even demonstrate a slight advantage or equality when compared to PatchTST.
Weaknesses: The storyline of this work appears to lack depth and insights. The inter-intra period encoder (I2PE) block, while not identical to the Timesnet architecture, is closely resemble it. Furthermore, I find it challenging to comprehend why the TDPD block and DCB block can be applied identically in both inter- and intra-period signals. My limited understanding is that the intra-period signal is simply the transpose of the inter-period signal, and I'm unsure of how the proposed winNet addresses the parallel implementation of TDPD and DCB in such a scenario. While the CNN model has potential as the author suggests, the novelty and motivation of this work seem weakly supported. A more comprehensive explanation and study of the design would be beneficial.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 2 fair