Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series

Autores
Barba Maggi, Lida
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
singular value decomposition
forecasting
linear models
wavelet decomposition
nonlinear models
singular spectrum analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/64919

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spelling Multiscale Forecasting Models Based on Singular Values for Nonstationary Time SeriesBarba Maggi, LidaCiencias Informáticassingular value decompositionforecastinglinear modelswavelet decompositionnonlinear modelssingular spectrum analysisTime series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/64919enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/CLTD/CLTD-01.pdfinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:08:54Zoai:sedici.unlp.edu.ar:10915/64919Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:08:54.532SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
title Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
spellingShingle Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
Barba Maggi, Lida
Ciencias Informáticas
singular value decomposition
forecasting
linear models
wavelet decomposition
nonlinear models
singular spectrum analysis
title_short Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
title_full Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
title_fullStr Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
title_full_unstemmed Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
title_sort Multiscale Forecasting Models Based on Singular Values for Nonstationary Time Series
dc.creator.none.fl_str_mv Barba Maggi, Lida
author Barba Maggi, Lida
author_facet Barba Maggi, Lida
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
singular value decomposition
forecasting
linear models
wavelet decomposition
nonlinear models
singular spectrum analysis
topic Ciencias Informáticas
singular value decomposition
forecasting
linear models
wavelet decomposition
nonlinear models
singular spectrum analysis
dc.description.none.fl_txt_mv Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Time series are valuable sources of information for supporting planning activities. Transport, fishery, economy and finances are predominant sectors concerned into obtaining information in advance to improve their productivity and efficiency. During the last decades diverse linear and nonlinear forecasting models have been developed for attending this demand. However the achievement of accuracy follows being a challenge due to the high variability of the most observed phenomena. In this research are proposed two decomposition methods based on Singular Value Decomposition of a Hankel matrix (HSVD) in order to extract components of low and high frequency from a nonstationary time series. The proposed decomposition is used to improve the accuracy of linear and nonlinear autoregressive models. The evaluation of the proposed forecasters is performed through data coming from transport sector and fishery sector. Series of injured persons in traffic accidents of Santiago and Valparaíso and stock of sardine and anchovy of central-south Chilean coast are used. Further, for comparison purposes, it is evaluated the forecast accuracy reached by two decomposition techniques conventionally used, Singular Spectrum Analysis (SSA) and decomposition based on Stationary Wavelet Transform (SWT), both joint with linear and nonlinear autoregressive models. The experiments shown that the proposed methods based on Singular Value Decomposition of a Hankel matrix in conjunction with linear or nonlinear models reach the best accuracy for one-step and multi-step ahead forecasting of the studied time series.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
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