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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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 |
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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 |
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