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An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK

Mark Evans Orcid Logo

Expert Systems with Applications, Volume: 1, Issue: 4, Pages: 1236 - 1244

Swansea University Author: Mark Evans Orcid Logo

DOI (Published version): 10.1016/j.eswa.2013.08.006

Abstract

Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric mo...

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Published in: Expert Systems with Applications
Published: 2014
URI: https://cronfa.swan.ac.uk/Record/cronfa15642
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first_indexed 2013-08-23T01:58:48Z
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spelling 2020-06-17T13:12:29.6579705 v2 15642 2013-08-22 An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK 7720f04c308cf7a1c32312058780d20c 0000-0003-2056-2396 Mark Evans Mark Evans true false 2013-08-22 MTLS Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions. Journal Article Expert Systems with Applications 1 4 1236 1244 31 3 2014 2014-03-31 10.1016/j.eswa.2013.08.006 COLLEGE NANME Materials Science and Engineering COLLEGE CODE MTLS Swansea University 2020-06-17T13:12:29.6579705 2013-08-22T17:05:17.9186263 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Mark Evans 0000-0003-2056-2396 1 0015642-20122017142744.pdf 15642.pdf 2017-12-20T14:27:44.7970000 Output 859941 application/pdf Accepted Manuscript true 2016-02-29T00:00:00.0000000 false eng
title An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
spellingShingle An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
Mark Evans
title_short An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
title_full An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
title_fullStr An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
title_full_unstemmed An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
title_sort An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK
author_id_str_mv 7720f04c308cf7a1c32312058780d20c
author_id_fullname_str_mv 7720f04c308cf7a1c32312058780d20c_***_Mark Evans
author Mark Evans
author2 Mark Evans
format Journal article
container_title Expert Systems with Applications
container_volume 1
container_issue 4
container_start_page 1236
publishDate 2014
institution Swansea University
doi_str_mv 10.1016/j.eswa.2013.08.006
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions.
published_date 2014-03-31T03:17:47Z
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score 11.017776

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