A novel distributed forecasting method based on information fusion and incremental learning for streaming time series

Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples...

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Detalhes bibliográficos
Autores: Melgar García, Laura, Gutiérrez-Avilés, David, Rubio-Escudero, Cristina, Troncoso, Alicia
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universidad Pablo de Olavide (UPO)
Repositorio:RIO. Repositorio Institucional Olavide
Idioma:inglés
OAI Identifier:oai:rio.upo.es:10433/25791
Acesso em linha:https://hdl.handle.net/10433/25791
Access Level:acceso abierto
Palavra-chave:Real-time forecasting
Incremental learning
Streaming time series
Electricity demand
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spelling A novel distributed forecasting method based on information fusion and incremental learning for streaming time seriesMelgar García, LauraGutiérrez-Avilés, DavidRubio-Escudero, CristinaTroncoso, AliciaReal-time forecastingIncremental learningStreaming time seriesElectricity demandReal-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples of information fusion is created with historical data. In particular, this model consists of the fusion of patterns composed of past values of the time series with the future values of their k-nearest neighbors. Afterwards, streaming data starts to arrive. The model is incrementally updated in the online stage using a buffer with streaming data that more accurately matches the current model patterns. The model can be updated daily, monthly, quarterly or based on error thresholds. The methodology has been applied to Spanish electricity demand time series providing more accurate results when the model is updated incrementally. The best error results are obtained with the daily update of the model, resulting in an error between 2% and 3.5% depending on the prediction horizon. The model provides better error results than other algorithms.Elsevier20262026-01-2320232023-07-2120232023-07-21journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/25791reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:rio.upo.es:10433/257912026-06-13T12:46:27Z
dc.title.none.fl_str_mv A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
title A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
spellingShingle A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
Melgar García, Laura
Real-time forecasting
Incremental learning
Streaming time series
Electricity demand
title_short A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
title_full A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
title_fullStr A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
title_full_unstemmed A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
title_sort A novel distributed forecasting method based on information fusion and incremental learning for streaming time series
dc.creator.none.fl_str_mv Melgar García, Laura
Gutiérrez-Avilés, David
Rubio-Escudero, Cristina
Troncoso, Alicia
author Melgar García, Laura
author_facet Melgar García, Laura
Gutiérrez-Avilés, David
Rubio-Escudero, Cristina
Troncoso, Alicia
author_role author
author2 Gutiérrez-Avilés, David
Rubio-Escudero, Cristina
Troncoso, Alicia
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Real-time forecasting
Incremental learning
Streaming time series
Electricity demand
topic Real-time forecasting
Incremental learning
Streaming time series
Electricity demand
description Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples of information fusion is created with historical data. In particular, this model consists of the fusion of patterns composed of past values of the time series with the future values of their k-nearest neighbors. Afterwards, streaming data starts to arrive. The model is incrementally updated in the online stage using a buffer with streaming data that more accurately matches the current model patterns. The model can be updated daily, monthly, quarterly or based on error thresholds. The methodology has been applied to Spanish electricity demand time series providing more accurate results when the model is updated incrementally. The best error results are obtained with the daily update of the model, resulting in an error between 2% and 3.5% depending on the prediction horizon. The model provides better error results than other algorithms.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-07-21
2023
2023-07-21
2026
2026-01-23
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10433/25791
url https://hdl.handle.net/10433/25791
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RIO. Repositorio Institucional Olavide
instname:Universidad Pablo de Olavide (UPO)
instname_str Universidad Pablo de Olavide (UPO)
reponame_str RIO. Repositorio Institucional Olavide
collection RIO. Repositorio Institucional Olavide
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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