PONE-D-25-41652

A Random Forest Regression-based Approach for Accurate Trade Balance Forecasting

PLOS ONE

Dear Dr. Ding,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Specifically:

Reject recommended as the article largely failed to meet PLOS One publication criteria 3 - Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail with reference to reviewers comments. Given the strict time frame, there is no certainty that the authors will be able to provide a robust revision to meet the above standard. More details: with reference to reviewers comments. Given the strict time frame, there is no certainty that the authors will be able to provide a robust revision to meet the above standard. More details: with reference to reviewers comments. Given the strict time frame, there is no certainty that the authors will be able to provide a robust revision to meet the above standard. More details: with reference to reviewers comments. Given the strict time frame, there is no certainty that the authors will be able to provide a robust revision to meet the above standard. More details: Criteria for Publication | PLOS One

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Kind regards,

Elochukwu Ukwandu, PhD

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?

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.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??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The author presented very interesting study in the domain of "A Random Forest Regression-based Approach for Accurate Trade Balance Forecasting" so i recommend Major revision before the final approval of the manuscript.

1- ROC and AUC are classification metrics, not regression metrics. Using them for regression (Section 5.6) is methodologically incorrect and shows conceptual misunderstanding, Kindly revise and correct it

2-The model explains only 25% of variance too low to claim accurate forecasting. conclusions overstate model performance.

3-The manuscript alternates between predicting “trade balance” and “export value.” Section 4.3 defines the target as export value, contradicting the abstract and other sections.

4-Despite poor R², the study repeatedly claims Random Forest “outperforms” deep models and provides “accurate predictions.

5-There’s no significance analysis for performance differences between models. kindly revise and correct it

6- Add acronyms section before the references.

7- Motivation behind the study is missing in the manuscript.

8-Negative R² values for DNN, Transformer, and Ensemble models (Table 1) indicate poor model fitting. Such results should trigger re-evaluation, not comparison for superiority.

Decision: Major Revision

Reviewer #2: This manuscript presents a comparative study of machine learning models for forecasting global trade balances. The topic is relevant, and the scale of the study (229 countries) is a notable contribution. However, the manuscript requires significant revisions to strengthen its methodological rigor, contextualization within the existing literature, and the depth of its analysis and discussion before it can be considered for publication.

1. Lack of Methodological and Literature Context:

Methodological Rationale: The manuscript would be significantly strengthened by including a dedicated section that justifies the selection of the specific machine learning models (e.g., DNN, Transformer, ensemble, and Random Forest). The authors should explain the anticipated strengths of each model in the context of trade data characteristics (e.g., non-linearity, high dimensionality, temporal aspects).

Critique of Previous ML Applications: The introduction and literature review should add more critical synthesis of previous studies that used ML for trade forecasting. Specifically, the authors should clearly delineate the common methodological limitations in prior work (e.g., over-reliance on single models, inadequate feature engineering, lack of interpretability) to better position their own research contributions.

2. Inadequate Description of Experimental Setup:

Data Partitioning and Validation: The manuscript misses a critical description of the data splitting strategy (e.g., train/validation/test sets) and the validation protocol. It is essential to detail whether a temporal split was used to preserve the time-series nature of the data and to explicitly state the measures taken to prevent data leakage, as this is fundamental to the credibility of the results.

3. Lack of Robustness and Interpretability Analysis:

Sensitivity Analysis: To validate the robustness of the findings, the authors should perform sensitivity analyses; like:

Geographical Robustness: Re-running the analysis on economically distinct subgroups (e.g., OECD vs. non-OECD nations).

Temporal Robustness: Evaluating model performance on different time periods to test for temporal consistency.

Methodological Robustness: Repeating the feature selection process with alternative methods to ensure the stability of the selected features.

Model Interpretability: The value of the study for policymakers and economists would be greatly enhanced by incorporating model interpretation techniques. Employing SHAP (SHapley Additive exPlanations) analysis or presenting normalized variable importance plots would help answer critical questions about which economic indicators are most influential globally and the direction of their impact on trade balances.

4. Insufficient Discussion and Acknowledgment of Limitations:

Discussion Section: The manuscript is currently missing a dedicated discussion section. This section should interpret the results in the context of the research questions and the existing literature. The striking performance gap between Random Forest and all other models (point #6) requires particularly careful interpretation.

Study Limitations: A balanced academic paper must acknowledge its limitations. The authors should discuss:

* The modest explanatory power (R² = 0.254) of the best model, indicating significant unexplained variance.

* The potential reasons for the catastrophic failure of complex models like DNN and Transformer.

* Challenges associated with data heterogeneity and imputation across 229 countries.

* The limitations of a static modeling approach that may not account for temporal dependencies and structural breaks.

5. Results Presentation and Clarity:

Table 1 & 2: The results show that all models except Random Forest perform catastrophically poorly (with extreme negative R² values). The authors must explicitly address this in the text, explaining that these models failed to outperform a simple mean baseline and discussing the potential reasons for such a significant performance disparity. Presenting this as a standard model comparison is misleading.

Figure 4: The x-axis labels are currently overlapping, obstructing visual understanding. This figure should be reformatted for clarity.

Figure 5: The caption for Figure 5 should be clarified. It is currently unclear what the "country feature" represents—does it indicate results for a specific country, or a specific feature related to countries?

**Recommendation**

**Major Revision.** The manuscript has the potential to be a valuable contribution but requires substantial revisions to address the major concerns outlined above, particularly regarding methodological justification, validation, interpretability, and the discussion of results and limitations.

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Reviewer #1: No

Reviewer #2: No

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For journal use only: PONEDEC3

Dear Dr. Ding,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 27 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

John Sum, Ph.D.

Academic Editor

PLOS One

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Additional Editor Comments (if provided):

As a reviewer still has some comments, the authors please revise your manuscript in accordance with the comments addressed.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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

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.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??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: I accept the article as authors addressed my all the comments carefully, no further comments from my side.

Reviewer #2: Thank you for your efforts in revising the manuscript. I believe the work is now very close to being suitable for publication. However, I have identified a few minor issues that should be addressed to improve the overall presentation:

Typographical Error: There is a spelling error in Figure 1. Please review the figure caption or the text within the figure for the indicated misspelling and correct it.

Figure Readability: In Figure 3, the text/levels appear to be overlapping, which makes them difficult to read. Please adjust the layout, font size, or spacing to ensure all elements are clearly legible.

Manuscript Length / Supplementary Material: The main text currently contains a high density of plots and tables. To improve the flow for the reader, I recommend moving some of the less critical plots or supplementary analyses to the Supplementary Materials section, keeping only the most essential figures in the main body.

Strengthening the Discussion: The final paragraph of the Discussion section currently makes a broad claim about the model's potential for policy analysis. To strengthen this, the authors should support their findings by citing specific references. For example, rather than stating that the findings are "consistent with emerging literature," please cite 1-2 specific papers that have similarly applied graph-based methods to economic or trade forecasting problems. This will ground your claim in the existing body of work.

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what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). 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 For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: No

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A GNN-based Approach for Accurate Trade Balance Forecasting and Interpretable Analysis

PONE-D-25-41652R2

Dear Dr. Ding,

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,

John Sum, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: