Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection

Qihang Zhou; Changjun Zhou; Xiao Wang

doi:10.1371/journal.pone.0262501

Peer Review History

Original SubmissionJune 28, 2021
23 Jul 2021 Decision Letter - Tao Song, Editor PONE-D-21-21056 Stock Prediction Based on Bidirectional Gated Recurrent Unit with Convolutional Neural Network and Feature Selection PLOS ONE Dear Dr. Zhou, 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 Sep 06 2021 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. 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The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please remove your figures from within your manuscript file, leaving only the individual TIFF/EPS image files, uploaded separately. These will be automatically included in the reviewers’ PDF. [Note: HTML markup is below. Please do not edit.] 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 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: No ****** 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 ****** 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 questions raised in the introduction are properly answered in the conclusions. The results are efficient and promising. It would be better if more literature could cited to support the relevant studies. It is suggested to cite the following literature to enrich the related work. Song, T.; Wang, Z.; Xie, P.; A novel dual path gated recurrent unit model for sea surface salinity prediction. Journal of At-mospheric and Oceanic Technology, 2020, 37, 317-325. Pang S, Xie P, Xu D, Meng F, Tao X, Li B, Li Y, Song T. NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning. Remote Sensing. 2021; 13(9):1860. https://doi.org/10.3390/rs13091860 Meng F, Song T, Xu D, et al. Forecasting tropical cyclones wave height using bidirectional gated recurrent unit[J]. Ocean Engineering, 2021: 108795. Song, T.; Jiang, J.; Li, W. A deep learning method with merged LSTM Neural Networks for SSHA Prediction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, 2853-2860. Also, the font colour in Figure 2 is too light and it is recommended that it be changed to a darker colour. Besides, in the acknowledgements section, it is suggested that the specific work of the individual authors be listed. Reviewer #2: 1、Many words in the text have “-“ in the middle, such as “variable” in line 31, line 36 “effectively”, line 66 “increase”, line 82 “follow”, etc. In line 43, the first letter of “The” should not be capitalized. Abstract： 2、The second sentence in the abstract wants to express the advantages of deep learning over traditional machine learning, so the comparative level is more appropriate here. "Achieve good fitting effect" is suggested to be changed to "achieve better fitting effect". 3、In the final description of the abstract, you can use refined language to add some information, such as which single comparison models were used, and the specific results show that the mixed model is better. Research Methodology- Feature Selection (FS)- Filter Selection： 4、The explanation of formula (1) in Filter Selection is not clear enough. Only the meaning of x_i^j is explained in the article, and the other variables are not explained. Method Based on FS-CNN-BGRU： 5、 The FS-CNN-BGRU method introduced in lines 177-189 only introduces the general process, but the organization and description of the steps are not perfect. It is recommended that you start with a subtitle for each step, and then introduce this step, and the format of the subtitle is as uniform as possible. The fourth step of the general process is the establishment of the FS-CNN-BGRU model. Although the specific process has been described in the text above, a simple summary here will make the description of the steps more complete. Experiment- The Construction of Model： 6、The segment of line 252 is too long. It is recommended to segment after “In” at the end of the line. Experiment- Evaluating Indicator： 7、Why choose two evaluation indicators: MAPE and R^2？Which aspects of the model are evaluated separately by these two evaluation indicators? Experimental Result： 8、There are no small headings in the experimental results. In order to make the structure clearer, the part should be divided into small headings according to different experimental settings. 9、Figure 5 ~ 12 did not find the corresponding explanation or reference in the text. 10、The optimal setting of the number of GRU units in the hybrid model is determined according to LSTM model. Why not directly use GRU to experiment and determine the optimal number of units? 11、The time step of the experiment is set to 1 day for forecast of 50 days. How is the time step of 50 days determined? If other step size FS-CNN-BGRU models are used, can they still achieve the best results? ****** 6. 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 [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0262501.r001
Revision 1
31 Jul 2021 Author Response Dear editor and reviewers: Thank you for your letter and the reviewers’ comments on our manuscript entitled" Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection". Your comments are of great help and guiding significance for improving the quality of this paper, improving the author's writing level and future scientific research. We have carefully studied the reviewers' comments and made corresponding amendments. Meanwhile, we apologize to the reviewers for the trouble caused by this manuscript. The reply to the comments is as follows (the reply is shown in blue). Replies to the reviewers’ comments: Reviewer #1: 1). The questions raised in the introduction are properly answered in the conclusions. The results are efficient and promising. It would be better if more literature could cited to support the relevant studies. It is suggested to cite the following literature to enrich the related work. Response: Thank you for your valuable suggestions on revising this manuscript. Now we have added the four references you mentioned to the manuscript to enrich the relevant work. See [5], [31], [33], [38] for details. 2). Also, the font colour in Figure 2 is too light and it is recommended that it be changed to a darker colour. Response: Responding to your request, we have changed the font color to a deeper color for viewing. 3). Besides, in the acknowledgements section, it is suggested that the specific work of the individual authors be listed. Response: Your suggestion is very useful to us. We have added the relevant information of the three authors mentioned in the manuscript to the acknowledgements section. Reviewer #2: 1). Many words in the text have “-“ in the middle, such as “variable” in line 31, line 36 “effectively”, line 66 “increase”, line 82 “follow”, etc. In line 43, the first letter of “The” should not be capitalized. Response: Thank you very much for pointing out the problems for this manuscript. This is our negligence, and we apologize. Now we have examined the whole manuscript and removed irrelevant information. 2). The second sentence in the abstract wants to express the advantages of deep learning over traditional machine learning, so the comparative level is more appropriate here. "Achieve good fitting effect" is suggested to be changed to "achieve better fitting effect". Response: Thank you for your suggestion. We have changed "achieve good fitting effect" to "achieve better fitting effect". 3). In the final description of the abstract, you can use refined language to add some information, such as which single comparison models were used, and the specific results show that the mixed model is better. Response: Your modification comments are very valuable to us, Thank you very much. We added the following information to the final description of the summary: In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). 4). The explanation of formula (1) in Filter Selection is not clear enough. Only the meaning of x_i^j is explained in the article, and the other variables are not explained. Response: The lack of other relevant information about Formula 1 is our problem. So we add the following statement to explain other variables in the manuscript: (1) The construction method is as follows: Selecting the nearest neighbor $x_{i, n h}$ from the similar samples of $x_i$ , which is called guessing the nearest neighbor. Selecting a nearest neighbor $x_{i, n m}$ in heterogeneous samples is called guessing the wrong nearest neighbor. (2) It can be seen from the above formula that for the value corresponding to a feature, the closer the same kind is, the farther the different kind is, and the larger corresponding statistics will be. 5). The FS-CNN-BGRU method introduced in lines 177-189 only introduces the general process, but the organization and description of the steps are not perfect. It is recommended that you start with a subtitle for each step, and then introduce this step, and the format of the subtitle is as uniform as possible. The fourth step of the general process is the establishment of the FS-CNN-BGRU model. Although the specific process has been described in the text above, a simple summary here will make the description of the steps more complete. Response: Thank you for your valuable comments. Now we have deleted the contents of lines 177-189 and put them into the "Experiment". 6). The segment of line 252 is too long. It is recommended to segment after “In” at the end of the line. Response: Responding to your request, we have listed "In" and the content after "In" as a separate paragraph. 7). Why choose two evaluation indicators: MAPE and R^2？Which aspects of the model are evaluated separately by these two evaluation indicators? Response: We are very sorry for your questions about this manuscript. We have made the following explanations: (1) during the experiment, if other evaluation indexes are used, the following problems may occur: if the stock price of the selected stock is not high, the accuracy of the model is not high, but the results calculated by using other evaluation indexes may be very good. On the contrary, if the stock is very high, the accuracy of the model is also very high, but the calculated results are not good, In other words, the quality of the model completely depends on the stock price, which is unacceptable. Therefore, an objective evaluation index should be selected to measure the quality of the model, so MAPE is selected. However, a single MAPE can not well evaluate the quality of the model, so R^2 is added. (2) The full name of MAPE is Mean Absolute Error. MAPE evaluates the accuracy of the model. 0% is the perfect model and 100% is the inferior model. The closer it is to 0%, the better. R ^ 2 also evaluates the accuracy of the model. 0 is the inferior model and 1 is the perfect model. The closer it is to 1, the better. These two evaluation indicators can accurately evaluate the advantages and disadvantages of a model. 8). There are no small headings in the experimental results. In order to make the structure clearer, the part should be divided into small headings according to different experimental settings. Response: Your suggestions are very helpful to improve the quality of our manuscript. Thank you for your suggestions. We have added the following small headings according to your requirements: Determine the number of convolution kernels, Determine the number of lstm units and Comparison of results of various models. 9). Figure 5 ~ 12 did not find the corresponding explanation or reference in the text. Response: This is our negligence. We apologize again. Now we have put "The forecast chart (real price and prediction price) of the eight stocks are shown in Figure 5 to 12" in lines 363-364 of the manuscript. 10). The optimal setting of the number of GRU units in the hybrid model is determined according to LSTM model. Why not directly use GRU to experiment and determine the optimal number of units? Response: Thank you for your question. Our answers to your question are as follows: because the model used in the comparative paper [14] is LSTM and GRU is a variant of LSTM, so the LSTM model is used to determine the best setting of the number of GRU units in the hybrid model. 11). The time step of the experiment is set to 1 day for forecast of 50 days. How is the time step of 50 days determined? If other step size FS-CNN-BGRU models are used, can they still achieve the best results? Response: We are very sorry for your confusion in this regard. We select the data from January 1, 1991 to December 31, 2020 as the data input (assuming 2000 days of data), and then randomly select a time step of 100, that is, use the data from the first day to the 100th day to predict the data of 101 days, and so on to get the data of 2000 days - 100 days = 1900 days. Then, put the data of 1900 days into the model for prediction and compare the results. On this basis, we also use other time steps to predict the stock price. Other time steps sometimes perform well, but in general, for the eight stocks in the manuscript, 50 days is the best time step. Kind regards Qihang Zhou Email: 825605142@qq.com Xiao Wang Email: tianzhu213@zjnu.cn Corresponding author: Changjun Zhou E-mail address: zhou-chang231@163.com Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0262501.r002
27 Dec 2021 Decision Letter - Tao Song, Editor Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection PONE-D-21-21056R1 Dear Dr. Zhou, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Tao Song 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 ******** 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: Partly ****** 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 ****** 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: Every suggestion is taken seriously, which is a remarkable and admirable attitude. The Submission has been greatly improved and is worthy of publication. My advice is to accept this article as soon as possible. This paper will play an active and important role in the research of related fields. Reviewer #2: The author answered all my questions very specifically and I have no further comments, so I guess it's acceptable. ****** 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 https://doi.org/10.1371/journal.pone.0262501.r003
Formally Accepted
27 Jan 2022 Acceptance Letter - Tao Song, Editor PONE-D-21-21056R1 Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection Dear Dr. Zhou: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Tao Song Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0262501.r004

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