PONE-D-24-28946Estimation of moving average processes using novel autocorrelation function matching method extended to online estimation of non-stationary processesPLOS ONE

Dear Dr. Dodek,

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Arne Johannssen

Academic Editor

PLOS ONE

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Additional Editor Comments:

Please carefully address the issues raised by the reviewers before the paper can be reconsidered for potential publication in PONE.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript is well-organized and written, here are some recommendations to consider

1. The title is dense, it needs simplification or rephrasing to enhance clarity and impact.

2. The novelty of the approach and key findings need to be emphasized in the abstract.

3. While various methods are mentioned in the introduction section, the motivation for choosing the specific approach should be highlighted more explicitly.

4. Clearly state the assumptions made in the derivations (e.g., ergodicity, properties of the input noise).

5. Consider discussing how the choice of the forgetting factor (λ) affects the results and any guidelines for selecting it.

6. Discuss some limitations or potential areas for future research to provide a more balanced view.

Reviewer #2: One of the main concerns with the article is the lack of sufficient experimental validation to support the claim that the proposed method is suitable for real-time applications. Although the capability for online estimation is mentioned, there are no robust experiments demonstrating that the algorithm can operate in real-time scenarios with latency constraints and continuous processing. The authors should include experiments using real-time data to analyze the method's ability to adapt to rapid parameter changes under real-world, real-time conditions.

This leads to a second observation: the proposed method has been tested primarily in controlled environments and simulations with synthetically generated data. There is no evaluation of how the method behaves in the presence of real-world noise or under less ideal conditions common in practical applications.

It is recommended to:

- Include tests with real datasets that introduce noise, missing data, or nonlinearities that could affect parameter estimation.

- Conduct a sensitivity analysis of the method with respect to different levels of noise and data variations to demonstrate that the algorithm is stable and capable of handling real-world situations.

Furthermore, the article makes strong claims about the reduction of computational complexity of the methodology, especially in the context of online estimation of non-stationary processes. However, a detailed analysis of the algorithm's time and space complexity is not provided, nor is there a quantitative comparison with other methods. It is advisable to compare the performance of the proposed method with other techniques, such as pseudolinear regression and prediction error-based methods, through quantitative benchmarking experiments.

Finally, although the proposed method is shown to outperform other methods in terms of estimation accuracy, the inclusion of additional statistical tests—such as hypothesis tests or significance analyses—would help to more rigorously validate that the observed differences between methods are statistically significant and not simply a product of data variability.

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Reviewer #1: No

Reviewer #2: No

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Estimation of stationary and non-stationary moving average processes in the correlation domain

PONE-D-24-28946R1

Dear Dr. Dodek,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Arne Johannssen

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thanks for all the modifications and taking care of the comments. just please make sure that the number and the referencing of equations are correct and the equations are well formatted.

Reviewer #2: I think that the revisions made fully address the concerns raised and enhance the quality of the manuscript. The additional experiments and detailed analysis provide greater clarity and confidence in the presented results. I am satisfied with the revision in this regard and consider that the text now meets the expected standards.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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