Methodology for processing time series using machine learning
There are currently countless applications that can be cited in different areas of research and industry, where the data are represented in the form of time series. In the last few years, a dramatic explosion in the amount of time series ha occurred, so their analysis plays a very important role, si...
- Autores:
-
Varela, Noel
Ospino, Cesar
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7661
- Acceso en línea:
- https://hdl.handle.net/11323/7661
https://doi.org/10.1016/j.procs.2020.07.096
https://repositorio.cuc.edu.co/
- Palabra clave:
- Unsupervised classifier
Time series
Assembly of grouping algorithms
- Rights
- openAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_591d9c91729d37d929cee9212041eeab |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/7661 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Methodology for processing time series using machine learning |
| title |
Methodology for processing time series using machine learning |
| spellingShingle |
Methodology for processing time series using machine learning Unsupervised classifier Time series Assembly of grouping algorithms |
| title_short |
Methodology for processing time series using machine learning |
| title_full |
Methodology for processing time series using machine learning |
| title_fullStr |
Methodology for processing time series using machine learning |
| title_full_unstemmed |
Methodology for processing time series using machine learning |
| title_sort |
Methodology for processing time series using machine learning |
| dc.creator.fl_str_mv |
Varela, Noel Ospino, Cesar Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Varela, Noel Ospino, Cesar Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Unsupervised classifier Time series Assembly of grouping algorithms |
| topic |
Unsupervised classifier Time series Assembly of grouping algorithms |
| description |
There are currently countless applications that can be cited in different areas of research and industry, where the data are represented in the form of time series. In the last few years, a dramatic explosion in the amount of time series ha occurred, so their analysis plays a very important role, since it permits to understand the phenomena described. A "time series" is a set of data of a certain phenomenon or equation, sequentially recorded. An alternative that allows to know the behavior and dynamics of a set of time series has been presented in the problem of classification, however, it is necessary to mention that most of the phenomena found in real life do not have a classification and that is why the unsupervised classification has brought great interest. Classification is organizing and categorizing objects into different, unlabeled classes or groups, which must be coherent or homogeneous [1][2]. This research proposes a methodology for obtaining the unsupervised classification of a set of time series using an unsupervised approach. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-01-05T21:46:12Z |
| dc.date.available.none.fl_str_mv |
2021-01-05T21:46:12Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
1877-0509 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/7661 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1016/j.procs.2020.07.096 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/7661 https://doi.org/10.1016/j.procs.2020.07.096 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677. [2] Hunter, F. D., Mitchard, E. T., Tyrrell, P., & Russell, S. (2020). Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem. Remote Sensing, 12(1), 198. [3] Yan, K., Huang, J., Shen, W., & Ji, Z. (2020). Unsupervised learning for fault detection and diagnosis of air handling units. Energy and Buildings, 210, 109689. [4] Franceschi, J. Y., Dieuleveut, A., & Jaggi, M. (2019). Unsupervised scalable representation learning for multivariate time series. In Advances in Neural Information Processing Systems (pp. 4652-4663). [5] Paris, C., Bruzzone, L., & Fernández-Prieto, D. (2019). A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4259-4277. [6] Wang, S., Azzari, G., & Lobell, D. B. (2019). Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote sensing of environment, 222, 303-317. [7] Viloria, A., Sierra, D. M., de la Hoz, L., Bohórquez, M. O., Bilbao, O. R., Pichón, A. R., … Hernández-Palma, H. (2020). NoSQL Database for Storing Historic Records in Monitoring Systems: Selection Process. In Advances in Intelligent Systems and Computing (Vol. 1039, pp. 336–344). Springer. https://doi.org/10.1007/978-3-030-30465-2_38 [8] Bode, G., Schreiber, T., Baranski, M., & Müller, D. (2019). A time series clustering approach for Building Automation and Control Systems. Applied energy, 238, 1337-1345. [9] Ukil, A., Bandyopadhyay, S., & Pal, A. (2019, July). DyReg-FResNet: Unsupervised Feature Space Amplified Dynamic Regularized Residual Network for Time Series Classification. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). [10] Kim, H., Kim, H. K., Kim, M., Park, J., Cho, S., Im, K. B., & Ryu, C. R. (2019). Representation learning for unsupervised heterogeneous multivariate time series segmentation and its application. Computers & Industrial Engineering, 130, 272-281. [11] Modak, S., Chattopadhyay, T., & Chattopadhyay, A. K. (2020). Unsupervised classification of eclipsing binary light curves through kmedoids clustering. Journal of Applied Statistics, 47(2), 376-392. [12] Punmiya, R., Zyabkina, O., Choe, S., & Meyer, J. (2019, June). Anomaly Detection in Power Quality Measurements Using Proximity-Based Unsupervised Machine Learning Techniques. In 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM) (pp. 1-6). IEEE. [13] Ryabko, D. (2019). Time-series information and unsupervised learning of representations. IEEE Transactions on Information Theory. [14] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677. [15] Viloria, A., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Stanescu, C. L. V., & Crissien, T. (2020). Machine Learning Applied to the H Index of Colombian Authors with Publications in Scopus. In Smart Innovation, Systems and Technologies (Vol. 167, pp. 388–397). Springer. https://doi.org/10.1007/978-981-15-1564-4_36. [16] Pereira, J., & Silveira, M. (2019, February). Learning representations from healthcare time series data for unsupervised anomaly detection. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1-7). IEEE. |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.source.spa.fl_str_mv |
Procedia Computer Science |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://www.sciencedirect.com/science/article/pii/S1877050920317968 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/2d6e50ba-34c1-4179-81c0-1e8f9911dce1/download https://repositorio.cuc.edu.co/bitstreams/01db9446-b3d4-4aa2-8750-332547682215/download https://repositorio.cuc.edu.co/bitstreams/a47a23e1-b472-4554-a2ee-03663e5e77a2/download https://repositorio.cuc.edu.co/bitstreams/bf590ec6-db7c-4d26-bb22-0e77d4e15b7a/download https://repositorio.cuc.edu.co/bitstreams/4a4d9c15-9b94-48ac-9cf7-2246b0d59da2/download |
| bitstream.checksum.fl_str_mv |
1da00e15d406b99ea9a7b0cc505e4f11 42fd4ad1e89814f5e4a476b409eb708c e30e9215131d99561d40d6b0abbe9bad 95b7392f5f2264882a0e120ee9aab9e8 9c2bdca148bfe4fe76338065e21f7be7 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1828166879168954368 |
| spelling |
Varela, NoelOspino, CesarPineda Lezama, Omar Bonerge2021-01-05T21:46:12Z2021-01-05T21:46:12Z20201877-0509https://hdl.handle.net/11323/7661https://doi.org/10.1016/j.procs.2020.07.096Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/There are currently countless applications that can be cited in different areas of research and industry, where the data are represented in the form of time series. In the last few years, a dramatic explosion in the amount of time series ha occurred, so their analysis plays a very important role, since it permits to understand the phenomena described. A "time series" is a set of data of a certain phenomenon or equation, sequentially recorded. An alternative that allows to know the behavior and dynamics of a set of time series has been presented in the problem of classification, however, it is necessary to mention that most of the phenomena found in real life do not have a classification and that is why the unsupervised classification has brought great interest. Classification is organizing and categorizing objects into different, unlabeled classes or groups, which must be coherent or homogeneous [1][2]. This research proposes a methodology for obtaining the unsupervised classification of a set of time series using an unsupervised approach.Varela, NoelOspino, CesarPineda Lezama, Omar Bonergeapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317968Unsupervised classifierTime seriesAssembly of grouping algorithmsMethodology for processing time series using machine learningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677.[2] Hunter, F. D., Mitchard, E. T., Tyrrell, P., & Russell, S. (2020). Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem. Remote Sensing, 12(1), 198.[3] Yan, K., Huang, J., Shen, W., & Ji, Z. (2020). Unsupervised learning for fault detection and diagnosis of air handling units. Energy and Buildings, 210, 109689.[4] Franceschi, J. Y., Dieuleveut, A., & Jaggi, M. (2019). Unsupervised scalable representation learning for multivariate time series. In Advances in Neural Information Processing Systems (pp. 4652-4663).[5] Paris, C., Bruzzone, L., & Fernández-Prieto, D. (2019). A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4259-4277.[6] Wang, S., Azzari, G., & Lobell, D. B. (2019). Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote sensing of environment, 222, 303-317.[7] Viloria, A., Sierra, D. M., de la Hoz, L., Bohórquez, M. O., Bilbao, O. R., Pichón, A. R., … Hernández-Palma, H. (2020). NoSQL Database for Storing Historic Records in Monitoring Systems: Selection Process. In Advances in Intelligent Systems and Computing (Vol. 1039, pp. 336–344). Springer. https://doi.org/10.1007/978-3-030-30465-2_38[8] Bode, G., Schreiber, T., Baranski, M., & Müller, D. (2019). A time series clustering approach for Building Automation and Control Systems. Applied energy, 238, 1337-1345.[9] Ukil, A., Bandyopadhyay, S., & Pal, A. (2019, July). DyReg-FResNet: Unsupervised Feature Space Amplified Dynamic Regularized Residual Network for Time Series Classification. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8).[10] Kim, H., Kim, H. K., Kim, M., Park, J., Cho, S., Im, K. B., & Ryu, C. R. (2019). Representation learning for unsupervised heterogeneous multivariate time series segmentation and its application. Computers & Industrial Engineering, 130, 272-281.[11] Modak, S., Chattopadhyay, T., & Chattopadhyay, A. K. (2020). Unsupervised classification of eclipsing binary light curves through kmedoids clustering. Journal of Applied Statistics, 47(2), 376-392.[12] Punmiya, R., Zyabkina, O., Choe, S., & Meyer, J. (2019, June). Anomaly Detection in Power Quality Measurements Using Proximity-Based Unsupervised Machine Learning Techniques. In 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM) (pp. 1-6). IEEE.[13] Ryabko, D. (2019). Time-series information and unsupervised learning of representations. IEEE Transactions on Information Theory.[14] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677.[15] Viloria, A., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Stanescu, C. L. V., & Crissien, T. (2020). Machine Learning Applied to the H Index of Colombian Authors with Publications in Scopus. In Smart Innovation, Systems and Technologies (Vol. 167, pp. 388–397). Springer. https://doi.org/10.1007/978-981-15-1564-4_36.[16] Pereira, J., & Silveira, M. (2019, February). Learning representations from healthcare time series data for unsupervised anomaly detection. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1-7). IEEE.PublicationORIGINALMethodology for processing time series using machine learning.pdfMethodology for processing time series using machine learning.pdfapplication/pdf1225005https://repositorio.cuc.edu.co/bitstreams/2d6e50ba-34c1-4179-81c0-1e8f9911dce1/download1da00e15d406b99ea9a7b0cc505e4f11MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/01db9446-b3d4-4aa2-8750-332547682215/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/a47a23e1-b472-4554-a2ee-03663e5e77a2/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMethodology for processing time series using machine learning.pdf.jpgMethodology for processing time series using machine learning.pdf.jpgimage/jpeg44560https://repositorio.cuc.edu.co/bitstreams/bf590ec6-db7c-4d26-bb22-0e77d4e15b7a/download95b7392f5f2264882a0e120ee9aab9e8MD54TEXTMethodology for processing time series using machine learning.pdf.txtMethodology for processing time series using machine learning.pdf.txttext/plain22559https://repositorio.cuc.edu.co/bitstreams/4a4d9c15-9b94-48ac-9cf7-2246b0d59da2/download9c2bdca148bfe4fe76338065e21f7be7MD5511323/7661oai:repositorio.cuc.edu.co:11323/76612024-09-17 14:21:15.15http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |