dc.creator | Torres, J. F. | es |
dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.date.accessioned | 2022-04-06T09:31:23Z | |
dc.date.available | 2022-04-06T09:31:23Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Torres, J.F., Gutiérrez Avilés, D., Troncoso, A. y Martínez Álvarez, F. (2019). Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting. En IWANN 2019 : 15th International Work-Conference on Artificial Neural Networks (259-269), Gran Canaria, España: Springer. | |
dc.identifier.isbn | 978-3-030-20520-1 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131796 | |
dc.description.abstract | In this paper, we introduce a deep learning approach, based on feed-forward
neural networks, for big data time series forecasting with arbitrary prediction horizons.
We firstly propose a random search to tune the multiple hyper-parameters involved in
the method perfor-mance. There is a twofold objective for this search: firstly, to improve
the forecasts and, secondly, to decrease the learning time. Next, we pro-pose a procedure
based on moving averages to smooth the predictions obtained by the different models
considered for each value of the pre-diction horizon. We conduct a comprehensive
evaluation using a real-world dataset composed of electricity consumption in Spain,
evaluating accuracy and comparing the performance of the proposed deep learning with
a grid search and a random search without applying smoothing. Reported results show
that a random search produces competitive accu-racy results generating a smaller
number of models, and the smoothing process reduces the forecasting error. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-1-R | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | IWANN 2019 : 15th International Work-Conference on Artificial Neural Networks (2019), pp. 259-269. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hyperparameters | es |
dc.subject | Time series forecasting | es |
dc.subject | Deep learning | es |
dc.title | Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting | 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-C2-1-R | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-20521-8_22 | es |
dc.identifier.doi | 10.1007/978-3-030-20521-8_22 | es |
dc.publication.initialPage | 259 | es |
dc.publication.endPage | 269 | es |
dc.eventtitle | IWANN 2019 : 15th International Work-Conference on Artificial Neural Networks | es |
dc.eventinstitution | Gran Canaria, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |