Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption

Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This ne...

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Autores: Pérez Chacón, Rubén, Asencio Cortés, Gualberto, Martínez-Álvarez, Francisco, Troncoso, Alicia
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Pablo de Olavide (UPO)
Repositorio:RIO. Repositorio Institucional Olavide
Idioma:inglés
OAI Identifier:oai:rio.upo.es:10433/19791
Acceso en línea:https://hdl.handle.net/10433/19791
Access Level:acceso abierto
Palabra clave:Multivariate analysis
Big Data
Time Series Forecasting
Pattern Sequence Forecasting
Electricity Consumption
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spelling Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumptionPérez Chacón, RubénAsencio Cortés, GualbertoMartínez-Álvarez, FranciscoTroncoso, AliciaMultivariate analysisBig DataTime Series ForecastingPattern Sequence ForecastingElectricity ConsumptionSeveral interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.Elsevier20242024-02-0620242024-01-2220242024-01-22journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/19791reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccessoai:rio.upo.es:10433/197912026-06-13T12:46:27Z
dc.title.none.fl_str_mv Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
title Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
spellingShingle Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
Pérez Chacón, Rubén
Multivariate analysis
Big Data
Time Series Forecasting
Pattern Sequence Forecasting
Electricity Consumption
title_short Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
title_full Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
title_fullStr Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
title_full_unstemmed Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
title_sort Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
dc.creator.none.fl_str_mv Pérez Chacón, Rubén
Asencio Cortés, Gualberto
Martínez-Álvarez, Francisco
Troncoso, Alicia
author Pérez Chacón, Rubén
author_facet Pérez Chacón, Rubén
Asencio Cortés, Gualberto
Martínez-Álvarez, Francisco
Troncoso, Alicia
author_role author
author2 Asencio Cortés, Gualberto
Martínez-Álvarez, Francisco
Troncoso, Alicia
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Multivariate analysis
Big Data
Time Series Forecasting
Pattern Sequence Forecasting
Electricity Consumption
topic Multivariate analysis
Big Data
Time Series Forecasting
Pattern Sequence Forecasting
Electricity Consumption
description Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-02-06
2024
2024-01-22
2024
2024-01-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10433/19791
url https://hdl.handle.net/10433/19791
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-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-sa/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-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-sa/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 15.300724

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