Show simple item record

dc.creatorVarela, Noel
dc.creatorOspino, Cesar
dc.creatorPineda Lezama, Omar Bonerge
dc.date.accessioned2021-01-05T21:46:12Z
dc.date.available2021-01-05T21:46:12Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7661
dc.description.abstractThere 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.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectUnsupervised classifierspa
dc.subjectTime seriesspa
dc.subjectAssembly of grouping algorithmsspa
dc.titleMethodology for processing time series using machine learningspa
dc.typearticlespa
dcterms.references[1] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677.spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[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).spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[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_38spa
dcterms.references[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.spa
dcterms.references[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).spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[13] Ryabko, D. (2019). Time-series information and unsupervised learning of representations. IEEE Transactions on Information Theory.spa
dcterms.references[14] Yan, S., Song, H., Li, N., Zou, L., & Ren, L. (2020). Improve Unsupervised Domain Adaptation with Mixup Training. arXiv preprint arXiv:2001.00677.spa
dcterms.references[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.spa
dcterms.references[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.spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317968spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.096


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal