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Based on the reviewer recommendation the paper is now acceptable.
[# PeerJ Staff Note - this decision was reviewed and approved by Sebastian Ventura, a PeerJ Section Editor covering this Section #]
The author addressed all comments, so I decided to accept the paper in its current form.
Ok
Enough
Nil
With reviewers comments add some resent work. I am also suggest to explain your results with more details
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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The author proposed a model called "A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN" The topic is interesting. However, there are a lot of shortcomings which need to be addressed, which are;
1. The authors need to rewrite the article in a completely different manner, concentrating on explaining their work's significance and contributions, which are very important to mention.
2. The data sets and parameters are not clearly set and mentioned in the proposed research.
3. The figures are very low quality; they need to be improved to better quality.
4. What is the novelty of the paper?
5. State-of-the-art literature is missing; the author needs to en-cooperate with the latest related work
6. There are many typos and grammatical mistakes that need to be addressed in revision.
Comments are in the main report.
Comments are in the main report.
No comments
1) Split the literature review and introduction into separate sections. Move Motivation, Contribution, and Paper Organization to the Introduction.
2) The current literature review is too limited. Include a broader range of papers.
3) Figures lack x-axis descriptions. Please add these details.
4) Kindly elaborate on your model's complexity and predictions. Explain the tables in the comparison analysis and include a cost analysis in performance evaluation.
5) I would suggest providing a detailed discussion on the hyperparameter tuning process for the LSTM and SVR models.
6) The source code was not found in the provided materials in the source files. Please include it for result transparency.
7) Add an analysis of the computation time and compare it with the previous schemes.
8) The current references are insufficient, with limited recent papers. Include more papers from 2023 and 2022 for up-to-date context. Currently, there is only one paper from 2023 and 4 papers from 2022.
All the comments are added in the basic reporting
All the comments are added in the basic reporting
All the comments are added in the basic reporting
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