Cargando…

Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test

Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the f...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Chuanjin, Zhang, Jing, Song, Fugen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032655/
https://www.ncbi.nlm.nih.gov/pubmed/24892061
http://dx.doi.org/10.1155/2014/621917
_version_ 1782317673224339456
author Jiang, Chuanjin
Zhang, Jing
Song, Fugen
author_facet Jiang, Chuanjin
Zhang, Jing
Song, Fugen
author_sort Jiang, Chuanjin
collection PubMed
description Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability.
format Online
Article
Text
id pubmed-4032655
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-40326552014-06-02 Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test Jiang, Chuanjin Zhang, Jing Song, Fugen ScientificWorldJournal Research Article Combination forecasting takes all characters of each single forecasting method into consideration, and combines them to form a composite, which increases forecasting accuracy. The existing researches on combination forecasting select single model randomly, neglecting the internal characters of the forecasting object. After discussing the function of cointegration test and encompassing test in the selection of single model, supplemented by empirical analysis, the paper gives the single model selection guidance: no more than five suitable single models can be selected from many alternative single models for a certain forecasting target, which increases accuracy and stability. Hindawi Publishing Corporation 2014 2014-04-22 /pmc/articles/PMC4032655/ /pubmed/24892061 http://dx.doi.org/10.1155/2014/621917 Text en Copyright © 2014 Chuanjin Jiang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jiang, Chuanjin
Zhang, Jing
Song, Fugen
Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title_full Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title_fullStr Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title_full_unstemmed Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title_short Selecting Single Model in Combination Forecasting Based on Cointegration Test and Encompassing Test
title_sort selecting single model in combination forecasting based on cointegration test and encompassing test
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032655/
https://www.ncbi.nlm.nih.gov/pubmed/24892061
http://dx.doi.org/10.1155/2014/621917
work_keys_str_mv AT jiangchuanjin selectingsinglemodelincombinationforecastingbasedoncointegrationtestandencompassingtest
AT zhangjing selectingsinglemodelincombinationforecastingbasedoncointegrationtestandencompassingtest
AT songfugen selectingsinglemodelincombinationforecastingbasedoncointegrationtestandencompassingtest