dc.creator | Melgar García, Laura | es |
dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.date.accessioned | 2022-04-06T09:15:20Z | |
dc.date.available | 2022-04-06T09:15:20Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Melgar García, L., Gutiérrez Avilés, D., Rubio Escudero, C. y Troncoso, A. (2021). Nearest Neighbors-Based Forecasting for Electricity Demand Time Series in Streaming. En CAEPIA 2021: 19th Conference of the Spanish Association for Artificial Intelligence (185-195), Málaga, España: Springer. | |
dc.identifier.isbn | 978-3-030-85712-7 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131791 | |
dc.description.abstract | This paper presents a new forecasting algorithm for time series in streaming
named StreamWNN. The methodology has two well-differentiated stages: the algorithm
searches for the nearest neighbors to generate an initial prediction model in the batch
phase. Then, an online phase is carried out when the time series arrives in streaming. In
par-ticular, the nearest neighbor of the streaming data from the training set is computed
and the nearest neighbors, previously computed in the batch phase, of this nearest
neighbor are used to obtain the predictions. Results using the electricity consumption
time series are reported, show-ing a remarkable performance of the proposed algorithm
in terms of fore-casting errors when compared to a nearest neighbors-based benchmark
algorithm. The running times for the predictions are also remarkable | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | CAEPIA 2021: 19th Conference of the Spanish Association for Artificial Intelligence (2021), pp. 185-195. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Forecasting | es |
dc.subject | Nearest neighbors | es |
dc.subject | Streaming time series | es |
dc.subject | Electricity demand | es |
dc.title | Nearest Neighbors-Based Forecasting for Electricity Demand Time Series 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 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-85713-4_18 | es |
dc.identifier.doi | 10.1007/978-3-030-85713-4_18 | es |
dc.publication.initialPage | 185 | es |
dc.publication.endPage | 195 | es |
dc.eventtitle | CAEPIA 2021: 19th Conference of the Spanish Association for Artificial Intelligence | es |
dc.eventinstitution | Málaga, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |