dc.creator | Lara Benítez, Pedro | es |
dc.creator | Carranza García, Manuel | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2022-02-17T12:21:02Z | |
dc.date.available | 2022-02-17T12:21:02Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Lara Benítez, P., Carranza García, M., Martínez Álvarez, F. y Riquelme Santos, J.C. (2020). On the performance of deep learning models for time series classification in streaming. En SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (144-154), Burgos, España: Springer. | |
dc.identifier.isbn | 978-3-030-57801-5 | es |
dc.identifier.issn | 2194-5357 | es |
dc.identifier.uri | https://hdl.handle.net/11441/130041 | |
dc.description.abstract | Processing data streams arriving at high speed requires the
development of models that can provide fast and accurate predictions.
Although deep neural networks are the state-of-the-art for many machine
learning tasks, their performance in real-time data streaming scenarios
is a research area that has not yet been fully addressed. Nevertheless,
there have been recent efforts to adapt complex deep learning models
for streaming tasks by reducing their processing rate. The design of the
asynchronous dual-pipeline deep learning framework allows to predict
over incoming instances and update the model simultaneously using two
separate layers. The aim of this work is to assess the performance of different
types of deep architectures for data streaming classification using
this framework. We evaluate models such as multi-layer perceptrons, recurrent,
convolutional and temporal convolutional neural networks over
several time-series datasets that are simulated as streams. The obtained
results indicate that convolutional architectures achieve a higher performance
in terms of accuracy and efficiency. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-2-R | es |
dc.description.sponsorship | Junta de Andalucía US-1263341 | es |
dc.description.sponsorship | Junta de Andalucía P18-RT-2778 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (2020), pp. 144-154. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Classification | es |
dc.subject | Data streaming | es |
dc.subject | Deep learning | es |
dc.subject | Time series | es |
dc.title | On the performance of deep learning models for time series classification in streaming | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2017-88209-C2-2-R | es |
dc.relation.projectID | US-1263341 | es |
dc.relation.projectID | P18-RT-2778 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-57802-2_14 | es |
dc.identifier.doi | 10.1007/978-3-030-57802-2_14 | es |
dc.publication.initialPage | 144 | es |
dc.publication.endPage | 154 | es |
dc.eventtitle | SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications | es |
dc.eventinstitution | Burgos, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |