A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting
Round 1
Reviewer 1 Report
This subject addressed is within the scope of the journal. However, the manuscript in the present version contains several problems. Appropriate revisions should be undertaken in order to justify recommendation for publication.
1. No decomposition method is used for decomposition to capture data noise. why? How will this affect the results? More details should be furnished.
2. It is mentioned that RBF and MLP are used as main model. What are the advantages of adopting this particular method over others in this case? How will this affect the results? More details should be furnished. Why not tried new advance hybrid models for comparison? For example,LSTM-ALO,ANFIS-GBO,ELM-PSOGWO,LSSVM-IMVO,SVR-SAMOA recently used in the literature of time series modeling. Should add these models recent literature and also explain why not adopted those advanced version?should apply a hybrid model..
3. For readers to quickly catch your contribution, it would be better to highlight major difficulties and challenges, and your original achievements to overcome them, in a clearer way in abstract and introduction.
4. It is mentioned that Poland is adopted as the case study. What are other feasible alternatives? What are the advantages of adopting this case study over others in this case? How will this affect the results? The authors should provide more details on this.
5. There is a serious concern regarding the novelty of this work. What new has been proposed?
6. Abstract needs to modify and to be revised to be quantitative. You can absorb readers' consideration by having some numerical results in this section.
7. There are some occasional grammatical problems within the text. It may need the attention of someone fluent in English language to enhance the readability.
8. Since the some figures have low-resolution printing, the reviewer cannot recognize them clearly. Please revise them with high resolution.
9. The discussion section in the present form is relatively weak and should be strengthened with more details and justifications.
10. In conclusion section, limitations and recommendations of this research should be highlighted.
11. The authors have to add the state-of-the art references in the manuscripts.
12. Some key parameters are not mentioned. The rationale on the choice of the set of parameters should be explained with more details. Have the authors experimented with other sets of values? What are the sensitivities of these parameters on the results?
13. It is mentioned that three performance indexes were used. What are the advantages of adopting these indexes over others (CC, willimot index) in this case? How will this affect the results? More details should be furnished.
14. Why not draw scatter, Taylor and violin plots to compare the results?should draw
Author Response
The answers are in the Word file.
Author Response File: 
Author Response.docx
Reviewer 2 Report
This manuscript with title of “A comparison of artificial neural network and time series models for timber price forecasting” has been investigated in detail. The topic addressed in the manuscript is potentially interesting and the manuscript contains some practical meanings, however, there are some issues which should be addressed by the authors:
· Please add appropriate graphs such as regression diagrams in order to describe the mentioned results clearer.
· Please add some numerical result to conclusion section.
· State exactly the main contributions of this work.
· The authors need to state how they solved the over-fitting and under-fitting problem of the neural network.
· How the best structure of MLP architecture was obtained (number of layers, number of neurons, epochs and etc.)? With which algorithm? Please describe in the text precisely.
· How the best result for RBF was obtained? Number of kernels, spread and etc. Please explain precisely in the manuscript.
· Please improve the quality of the figures. I cannot read several parts of them.
Author Response
The answers are in the Word file. Thank you.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The authors revised all comments properly.
Reviewer 2 Report
All of the comments have been applied in the manuscript. My recommendation is "Accept".
