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[REVIEW]: TimeSeriesClustering: An extensible framework in Julia #1573
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Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @jgoldfar, @ahwillia it looks like you're currently assigned to review this paper 🎉. ⭐ Important ⭐ If you haven't already, you should seriously consider unsubscribing from GitHub notifications for this (https://github.com/openjournals/joss-reviews) repository. As a reviewer, you're probably currently watching this repository which means for GitHub's default behaviour you will receive notifications (emails) for all reviews 😿 To fix this do the following two things:
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For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:
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Note: @ahwillia is traveling for the next few weeks. It will be about 3 weeks until he can work on this review. |
👋Thank you all for offering to review, I look forward to your comments. |
👋 @jgoldfar - have you had a chance to get started? |
Reminder set for @ahwillia in 7 days |
@jgoldfar - any update on this? |
Looking through this now. I'm generally very impressed and think we should be able to approve this in short order. I am confirming that I can install and execute the package now. My biggest piece of feedback is that the README and description of the package should emphasize even more applications. I think this package will be broadly useful to many fields! For example, the first sentence of the README might lead users to think the package is for a very specialized purpose I would recommend editing to something like... Current: "ClustForOpt is a julia implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems." Revised: "ClustForOpt is a julia implementation of unsupervised machine learning methods for detecting motifs, clustering, and quantifying similarity between time series datasets." Likewise, in the subsequent paragraphs, I recommend adding some more example applications with citations. Segmentation and clustering of audio datasets should be an easy one to find. It is of course okay to say something like "this package was originally developed for energy systems optimization" but I think emphasizing the generality of the package and the methods as much as possible will increase the impact of this work. Full disclosure, I've worked on using very simple time warping methods for neural data (https://www.biorxiv.org/content/10.1101/661165v1), though those data show very different statistics and call for different modeling approaches. But I'm quite enthusiastic about this area of research. |
One final thought, I don't insist on changing the name of the repo, but something like "TimeSeriesClustering.jl" would seem to better capture the function of the package. The name ClustForOpt doesn't make it super clear what the package does... Also can the authors comment on the differences between this package and other time series packages in julia (e.g |
👋 @ahwillia, please update us on how your review is going. |
Thank you for your feedback, these are great ideas! I am out for the weekend, and will get back to this next week. |
In case that there are any papers that you can recommend to read in the suggested application areas, suggestions are greatly appreciated. |
Checked my boxes...
Eamonn Keogh has a variety of methods and application papers to check out (e.g. https://www.cs.ucr.edu/~eamonn/MatrixProfile.html). I'm sure many other research groups have relevant papers as well. Please don't worry about being comprehensive, but the more references you can find the better. |
Can you please go ahead and do this? (and check the boxes for items that are complete) |
@whedon generate pdf from branch paper |
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Ok, that seems not to have worked. Will push to master branch and revisit. I will be updating the package to reflect its more general applicability and rename to TimeSeriesClustering.jl. I will update here once complete. |
@whedon generate pdf |
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@whedon set v0.5.2 as version |
OK. v0.5.2 is the version. |
@whedon set 10.5281/zenodo.3385349 as archive |
OK. 10.5281/zenodo.3385349 is the archive. |
@whedon accept |
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Check final proof 👉 openjournals/joss-papers#950 If the paper PDF and Crossref deposit XML look good in openjournals/joss-papers#950, then you can now move forward with accepting the submission by compiling again with the flag
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@whedon check references |
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👋 @holgerteichgraeber - It looks like the two missing DOIs found above are correct - please add them. And the invalid DOI is indeed invalid - either replace it with the right one or remove it. Then please regenerate the pdf. |
@whedon generate pdf |
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@danielskatz Thank you, I updated the two DOIs and removed the third. |
@whedon check references |
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@whedon accept deposit=true |
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🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦 |
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🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉 If you would like to include a link to your paper from your README use the following code snippets:
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Thank you @danielskatz @ahwillia @jgoldfar for editing and reviewing! I very much appreciate that you took the time to review, your comments were extremely helpful! |
Submitting author: @holgerteichgraeber (Holger Teichgraeber)
Repository: https://github.com/holgerteichgraeber/TimeSeriesClustering.jl
Version: v0.5.2
Editor: @danielskatz
Reviewer: @jgoldfar, @ahwillia
Archive: 10.5281/zenodo.3385349
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✨ Please try and complete your review in the next two weeks ✨
Review checklist for @jgoldfar
Conflict of interest
Code of Conduct
General checks
Functionality
Documentation
Software paper
paper.md
file include a list of authors with their affiliations?Review checklist for @ahwillia
Conflict of interest
Code of Conduct
General checks
Functionality
Documentation
Software paper
paper.md
file include a list of authors with their affiliations?The text was updated successfully, but these errors were encountered: