Skip to content

Commit

Permalink
Merge pull request #5195 from openjournals/joss.06294
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Mar 30, 2024
2 parents 4387469 + bca15d3 commit 2b8d347
Show file tree
Hide file tree
Showing 4 changed files with 741 additions and 0 deletions.
268 changes: 268 additions & 0 deletions joss.06294/10.21105.joss.06294.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,268 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240330T141705-0ea8bc011eff66513ce82ce067a9b9d231417e15</doi_batch_id>
<timestamp>20240330141705</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>03</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>95</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>fABBA: A Python library for the fast symbolic
approximation of time series</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Xinye</given_name>
<surname>Chen</surname>
<ORCID>https://orcid.org/0000-0003-1778-393X</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Stefan</given_name>
<surname>Güttel</surname>
<ORCID>https://orcid.org/0000-0003-1494-4478</ORCID>
</person_name>
</contributors>
<publication_date>
<month>03</month>
<day>30</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6294</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06294</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.10885652</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6294</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06294</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06294</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06294.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="EG19b">
<article_title>ABBA: adaptive Brownian bridge-based symbolic
aggregation of time series</article_title>
<author>Elsworth</author>
<journal_title>Data Mining and Knowledge
Discovery</journal_title>
<volume>34</volume>
<doi>10.1007/s10618-020-00689-6</doi>
<cYear>2020</cYear>
<unstructured_citation>Elsworth, S., &amp; Güttel, S.
(2020). ABBA: adaptive Brownian bridge-based symbolic aggregation of
time series. Data Mining and Knowledge Discovery, 34, 1175–1200.
https://doi.org/10.1007/s10618-020-00689-6</unstructured_citation>
</citation>
<citation key="1056489">
<article_title>Least squares quantization in
PCM</article_title>
<author>Lloyd</author>
<journal_title>Transactions on Information
Theory</journal_title>
<volume>28</volume>
<doi>10.1109/tit.1982.1056489</doi>
<cYear>1982</cYear>
<unstructured_citation>Lloyd, S. P. (1982). Least squares
quantization in PCM. Transactions on Information Theory, 28, 129–137.
https://doi.org/10.1109/tit.1982.1056489</unstructured_citation>
</citation>
<citation key="SAX03">
<article_title>A symbolic representation of time series,
with implications for streaming algorithms</article_title>
<author>Lin</author>
<journal_title>Proceedings of the 8th ACM SIGMOD workshop on
research issues in data mining and knowledge discovery</journal_title>
<doi>10.1145/882082.882086</doi>
<cYear>2003</cYear>
<unstructured_citation>Lin, J., Keogh, E., Lonardi, S.,
&amp; Chiu, B. (2003). A symbolic representation of time series, with
implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD
Workshop on Research Issues in Data Mining and Knowledge Discovery,
2–11. https://doi.org/10.1145/882082.882086</unstructured_citation>
</citation>
<citation key="CG22a">
<article_title>An efficient aggregation method for the
symbolic representation of temporal data</article_title>
<author>Chen</author>
<journal_title>ACM Transactions on Knowledge Discovery from
Data</journal_title>
<issue>1</issue>
<volume>17</volume>
<doi>10.1145/3532622</doi>
<cYear>2023</cYear>
<unstructured_citation>Chen, X., &amp; Güttel, S. (2023). An
efficient aggregation method for the symbolic representation of temporal
data. ACM Transactions on Knowledge Discovery from Data, 17(1), 1–22.
https://doi.org/10.1145/3532622</unstructured_citation>
</citation>
<citation key="TAKTAK2024102294">
<article_title>ECG classification with learning ensemble
based on symbolic discretization</article_title>
<author>Taktak</author>
<journal_title>Information Systems</journal_title>
<volume>120</volume>
<doi>10.1016/j.is.2023.102294</doi>
<cYear>2024</cYear>
<unstructured_citation>Taktak, M., Ltifi, H., &amp; Ayed, M.
B. (2024). ECG classification with learning ensemble based on symbolic
discretization. Information Systems, 120, 102294.
https://doi.org/10.1016/j.is.2023.102294</unstructured_citation>
</citation>
<citation key="10.1007/978-3-031-24378-3_4">
<article_title>Fast time series classification with random
symbolic subsequences</article_title>
<author>Nguyen</author>
<journal_title>Advanced analytics and learning on temporal
data: 7th ECML PKDD workshop</journal_title>
<doi>10.1007/978-3-031-24378-3_4</doi>
<cYear>2023</cYear>
<unstructured_citation>Nguyen, T. L., &amp; Ifrim, G.
(2023). Fast time series classification with random symbolic
subsequences. Advanced Analytics and Learning on Temporal Data: 7th ECML
PKDD Workshop, 50–65.
https://doi.org/10.1007/978-3-031-24378-3_4</unstructured_citation>
</citation>
<citation key="lin2007experiencing">
<article_title>Experiencing SAX: A novel symbolic
representation of time series</article_title>
<author>Lin</author>
<journal_title>Data Mining and Knowledge
Discovery</journal_title>
<issue>2</issue>
<volume>15</volume>
<doi>10.1007/s10618-007-0064-z</doi>
<cYear>2007</cYear>
<unstructured_citation>Lin, J., Keogh, E., Wei, L., &amp;
Lonardi, S. (2007). Experiencing SAX: A novel symbolic representation of
time series. Data Mining and Knowledge Discovery, 15(2), 107–144.
https://doi.org/10.1007/s10618-007-0064-z</unstructured_citation>
</citation>
<citation key="scikit-learn">
<article_title>Scikit-learn: Machine learning in
python</article_title>
<author>Pedregosa</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<issue>85</issue>
<volume>12</volume>
<cYear>2011</cYear>
<unstructured_citation>Pedregosa, F., Varoquaux, G.,
Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
Cournapeau, D., Brucher, M., Perrot, M., &amp; Duchesnay, Édouard.
(2011). Scikit-learn: Machine learning in python. Journal of Machine
Learning Research, 12(85), 2825–2830.
http://jmlr.org/papers/v12/pedregosa11a.html</unstructured_citation>
</citation>
<citation key="9935005">
<article_title>Foreseer: Efficiently forecasting malware
event series with long short-term memory</article_title>
<author>Gogineni</author>
<journal_title>IEEE international symposium on secure and
private execution environment design</journal_title>
<doi>10.1109/seed55351.2022.00016</doi>
<cYear>2022</cYear>
<unstructured_citation>Gogineni, K., Derasari, P., &amp;
Venkataramani, G. (2022). Foreseer: Efficiently forecasting malware
event series with long short-term memory. IEEE International Symposium
on Secure and Private Execution Environment Design, 97–108.
https://doi.org/10.1109/seed55351.2022.00016</unstructured_citation>
</citation>
<citation key="EG20b">
<article_title>Time series forecasting using LSTM networks:
A symbolic approach</article_title>
<author>Elsworth</author>
<doi>10.48550/arXiv.2003.05672</doi>
<cYear>2020</cYear>
<unstructured_citation>Elsworth, S., &amp; Güttel, S.
(2020). Time series forecasting using LSTM networks: A symbolic approach
(No. arXiv:2003.05672v1; p. 12).
https://doi.org/10.48550/arXiv.2003.05672</unstructured_citation>
</citation>
<citation key="WANG2023109123">
<article_title>Data-driven prognostics based on
time-frequency analysis and symbolic recurrent neural network for fuel
cells under dynamic load</article_title>
<author>Wang</author>
<journal_title>Reliability Engineering &amp; System
Safety</journal_title>
<volume>233</volume>
<doi>10.1016/j.ress.2023.109123</doi>
<cYear>2023</cYear>
<unstructured_citation>Wang, C., Dou, M., Li, Z., Outbib,
R., Zhao, D., Zuo, J., Wang, Y., Liang, B., &amp; Wang, P. (2023).
Data-driven prognostics based on time-frequency analysis and symbolic
recurrent neural network for fuel cells under dynamic load. Reliability
Engineering &amp; System Safety, 233, 109123.
https://doi.org/10.1016/j.ress.2023.109123</unstructured_citation>
</citation>
<citation key="10.1145/3448672">
<article_title>A framework for generating summaries from
temporal personal health data</article_title>
<author>Harris</author>
<journal_title>ACM Transactions on Computing for
Healthcare</journal_title>
<issue>3</issue>
<volume>2</volume>
<doi>10.1145/3448672</doi>
<cYear>2021</cYear>
<unstructured_citation>Harris, J. J., Chen, C.-H., &amp;
Zaki, M. J. (2021). A framework for generating summaries from temporal
personal health data. ACM Transactions on Computing for Healthcare,
2(3). https://doi.org/10.1145/3448672</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit 2b8d347

Please sign in to comment.