LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series

A 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...

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Detalles Bibliográficos
Autores: Martínez Álvarez, Francisco, Troncoso Lora, Alicia, Riquelme Santos, José Cristóbal, Aguilar Ruiz, Jesús Salvador
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2008
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/40507
Acceso en línea:http://hdl.handle.net/11441/40507
https://doi.org/10.1109/ICDM.2008.129
Access Level:acceso abierto
Palabra clave:Clustering
Forecasting
Neighbourhood
Time series
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spelling LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time SeriesMartínez Álvarez, FranciscoTroncoso Lora, AliciaRiquelme Santos, José CristóbalAguilar Ruiz, Jesús SalvadorClusteringForecastingNeighbourhoodTime seriesA 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.Lenguajes y Sistemas Informáticos2008info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/40507https://doi.org/10.1109/ICDM.2008.129reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)Inglés2008 Eighth IEEE International Conference on Data Mining, Dec. 2008, pp. 453 - 461info:eu-repo/semantics/openAccessoai:idus.us.es:11441/405072026-06-17T12:51:07Z
dc.title.none.fl_str_mv LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
title LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
spellingShingle LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
Martínez Álvarez, Francisco
Clustering
Forecasting
Neighbourhood
Time series
title_short LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
title_full LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
title_fullStr LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
title_full_unstemmed LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
title_sort LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series
dc.creator.none.fl_str_mv Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author Martínez Álvarez, Francisco
author_facet Martínez Álvarez, Francisco
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author_role author
author2 Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Aguilar Ruiz, Jesús Salvador
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Clustering
Forecasting
Neighbourhood
Time series
topic Clustering
Forecasting
Neighbourhood
Time series
description A 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.
publishDate 2008
dc.date.none.fl_str_mv 2008
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/40507
https://doi.org/10.1109/ICDM.2008.129
url http://hdl.handle.net/11441/40507
https://doi.org/10.1109/ICDM.2008.129
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 2008 Eighth IEEE International Conference on Data Mining, Dec. 2008, pp. 453 - 461
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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