PONE-D-20-39606

Calculation and Realization of New Method Grey Residual Error Correction Model

PLOS ONE

Dear Dr. Xiao,

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Academic Editor

PLOS ONE

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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: I Don't Know

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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: No

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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 paper proposed a method to improve the prediction accuracy of the grey model based on the residual error correction method, and this new method (i.e., the improved GM method) was applied in three experimental analyses from the perspective of simulating the stock price of PetroChina, the population of US and the per capita energy consumption of China, respectively. Moreover, evaluation index including average residual, mean relative error and posterior difference ratio were selected, when compared the traditional GM method with the new method established by the residual correction simulation method. The effort of the authors is greatly appreciated, but in my opinion, Partial content needs to be improved, my comments are as follows:

1. It is obvious that the improved GM method is capable of addressing the issue of few samples and poor information in economy, management and engineering technology compared to the GM method. However, high requirements on the input data were not emphatic in this paper, considering the input data such as the stock price, the population and the per capita energy consumption present similar linear features and have the same sign. However, some input data present waveform features, or even highly non-linear features in reality, which was neglected in this paper. Therefore, input data selected in three experimental analyses should be representative and different, in other words, it is better to have three different characteristics of data rather than the same characteristics of data. As a consequence, emphasize the characteristics of the input data and change the type of the experimental analysis are necessary.

2. Similar to other prediction methods, the GM method also bears the limitations. Hence, the issue of improving the accuracy has received considerable critical attention. Nevertheless, literature and review involving introducing the advances in the GM method is lacking, especially the research progress of foreign scholars in this field. In addition, residual GM method mentioned is a common method to amend the traditional GM method and have been adopted in many aspects. Therefore, the innovation of this new improved GM method is insufficient. As a contrast, some other improved GM methods need to be introduced to highlight the advantages of the improved GM method in this paper.

3. It is worth mentioning that evaluation index in this paper have no explicit formula expressions. Except for average residual, mean relative error (MRE) and posterior difference ratio mentioned in this paper, other indicators such as mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE) should be taken into consideration. Moreover, the flow chart of constructing the improved GM method is necessary, given the complexity of the formula derivation.

Given the above three comments, I advise the paper need major changes, adding some important references bibliography at the same time.

Reviewer #2: The paper is suggested to be accepted with major revision. The authors are suggested to follow up the below comments.

1)There is still room to improve English writing. Please improve English and engage native proof-reader if available.

There are too many grammatical errors, such as too many sentences without subject.

“According to the characteristics of the original data, improve the gray prediction algorithm, optimize the background values, optimize the initial conditions, etc.”

And please try more passive sentences.

2) It is beneficial to cite other related publications in Plus One to reflect the relevance of your submission.

3) The amount of data is too small, it is limited for the examples of experimental analysis.

4) It will be helpful to add a flow-process diagram for describing the new residual correction grey model.

5) By the example data analysis, it can be found that the fitting accuracy and prediction effect of this method are better than the traditional GM(1,1) model. But how does it compare with other improved models?

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: Reviewer Comment.docx

Calculation and Realization of New Method Grey Residual Error Correction Model

PONE-D-20-39606R1

Dear Dr. Xiao,

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,

Dragan Pamucar

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

**********

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: (No Response)

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

**********

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: The manuscript is acceptable and could be published in the journal PLOS ONE. Comments on the lack of a "discussion" point, would improve the readability of the manuscript, but their disregard by the authors does not negatively affect on the quality of the manuscript.

Reviewer #2: The authors have adequately addressed your comments raised in a previous round of review，and I feel that this manuscript is now acceptable for publication.

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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