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An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays
Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important n...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049131/ https://www.ncbi.nlm.nih.gov/pubmed/33954228 http://dx.doi.org/10.7717/peerj-cs.409 |
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author | Saghi, Faramarz Jahangoshai Rezaee, Mustafa |
author_facet | Saghi, Faramarz Jahangoshai Rezaee, Mustafa |
author_sort | Saghi, Faramarz |
collection | PubMed |
description | Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series. |
format | Online Article Text |
id | pubmed-8049131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80491312021-05-04 An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays Saghi, Faramarz Jahangoshai Rezaee, Mustafa PeerJ Comput Sci Algorithms and Analysis of Algorithms Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series. PeerJ Inc. 2021-04-07 /pmc/articles/PMC8049131/ /pubmed/33954228 http://dx.doi.org/10.7717/peerj-cs.409 Text en © 2021 Saghi and Jahangoshai Rezaee https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Saghi, Faramarz Jahangoshai Rezaee, Mustafa An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title | An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title_full | An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title_fullStr | An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title_full_unstemmed | An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title_short | An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
title_sort | ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049131/ https://www.ncbi.nlm.nih.gov/pubmed/33954228 http://dx.doi.org/10.7717/peerj-cs.409 |
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