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dc.creatorMartínez Álvarez, Franciscoes
dc.creatorTroncoso Lora, Aliciaes
dc.creatorRiquelme Santos, José Cristóbales
dc.creatorAguilar Ruiz, Jesús Salvadores
dc.date.accessioned2016-04-27T09:45:04Z
dc.date.available2016-04-27T09:45:04Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/11441/40507
dc.description.abstractA new approach is presented in this work with the aim of predicting time series behaviors. A previous labeling of the samples is obtained utilizing clustering techniques and the forecasting is applied using the information provided by the clustering. Thus, the whole data set is discretized with the labels assigned to each data point and the main novelty is that only these labels are used to predict the future behavior of the time series, avoiding using the real values of the time series until the process ends. The results returned by the algorithm, however, are not labels but the nominal value of the point that is required to be predicted. The algorithm based on labeled (LBF) has been tested in several energy-related time series and a notable improvement in the prediction has been achieved.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.relation.ispartof2008 Eighth IEEE International Conference on Data Mining, Dec. 2008, pp. 453 - 461es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectClusteringes
dc.subjectForecastinges
dc.subjectNeighbourhoodes
dc.subjectTime serieses
dc.titleLBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Serieses
dc.typeinfo:eu-repo/semantics/bookPartes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.identifier.doihttp://dx.doi.org/10.1109/ICDM.2008.129es
idus.format.extent8es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/40507

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