Journal article 144 views 18 downloads
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data
Journal of Forecasting
Swansea University Author: Mohammad Abedin
-
PDF | Version of Record
© 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.
Download (3.26MB)
DOI (Published version): 10.1002/for.3185
Abstract
Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision-makers, we apply data mining methods to extract the main influencing factors aff...
Published in: | Journal of Forecasting |
---|---|
ISSN: | 0277-6693 1099-131X |
Published: |
Wiley
2024
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa67179 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
first_indexed |
2024-07-25T10:34:05Z |
---|---|
last_indexed |
2024-07-25T10:34:05Z |
id |
cronfa67179 |
recordtype |
SURis |
fullrecord |
<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>67179</id><entry>2024-07-25</entry><title>A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data</title><swanseaauthors><author><sid>4ed8c020eae0c9bec4f5d9495d86d415</sid><ORCID></ORCID><firstname>Mohammad</firstname><surname>Abedin</surname><name>Mohammad Abedin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-07-25</date><deptcode>CBAE</deptcode><abstract>Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision-makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data-driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short-term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data-forecasting model-decision support” decision paradigm for real-world problems.</abstract><type>Journal Article</type><journal>Journal of Forecasting</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0277-6693</issnPrint><issnElectronic>1099-131X</issnElectronic><keywords/><publishedDay>15</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-08-15</publishedDate><doi>10.1002/for.3185</doi><url/><notes/><college>COLLEGE NANME</college><department>Management School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CBAE</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Swansea University;
National Natural Science Foundation of China - 72171184</funders><projectreference/><lastEdited>2024-08-29T16:26:31.2431207</lastEdited><Created>2024-07-25T11:31:54.4347821</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Huosong</firstname><surname>Xia</surname><order>1</order></author><author><firstname>Xiaoyu</firstname><surname>Hou</surname><order>2</order></author><author><firstname>Justin Zuopeng</firstname><surname>Zhang</surname><orcid>0000-0002-4074-9505</orcid><order>3</order></author><author><firstname>Mohammad</firstname><surname>Abedin</surname><orcid></orcid><order>4</order></author></authors><documents><document><filename>67179__31177__9129f72532a04075aa1a43d650e42c48.pdf</filename><originalFilename>67179.VoR.pdf</originalFilename><uploaded>2024-08-29T16:20:55.3439093</uploaded><type>Output</type><contentLength>3414698</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
spelling |
v2 67179 2024-07-25 A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data 4ed8c020eae0c9bec4f5d9495d86d415 Mohammad Abedin Mohammad Abedin true false 2024-07-25 CBAE Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision-makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data-driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short-term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data-forecasting model-decision support” decision paradigm for real-world problems. Journal Article Journal of Forecasting 0 Wiley 0277-6693 1099-131X 15 8 2024 2024-08-15 10.1002/for.3185 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University; National Natural Science Foundation of China - 72171184 2024-08-29T16:26:31.2431207 2024-07-25T11:31:54.4347821 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Huosong Xia 1 Xiaoyu Hou 2 Justin Zuopeng Zhang 0000-0002-4074-9505 3 Mohammad Abedin 4 67179__31177__9129f72532a04075aa1a43d650e42c48.pdf 67179.VoR.pdf 2024-08-29T16:20:55.3439093 Output 3414698 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/ |
title |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
spellingShingle |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data Mohammad Abedin |
title_short |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
title_full |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
title_fullStr |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
title_full_unstemmed |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
title_sort |
A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data |
author_id_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415 |
author_id_fullname_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin |
author |
Mohammad Abedin |
author2 |
Huosong Xia Xiaoyu Hou Justin Zuopeng Zhang Mohammad Abedin |
format |
Journal article |
container_title |
Journal of Forecasting |
container_volume |
0 |
publishDate |
2024 |
institution |
Swansea University |
issn |
0277-6693 1099-131X |
doi_str_mv |
10.1002/for.3185 |
publisher |
Wiley |
college_str |
Faculty of Humanities and Social Sciences |
hierarchytype |
|
hierarchy_top_id |
facultyofhumanitiesandsocialsciences |
hierarchy_top_title |
Faculty of Humanities and Social Sciences |
hierarchy_parent_id |
facultyofhumanitiesandsocialsciences |
hierarchy_parent_title |
Faculty of Humanities and Social Sciences |
department_str |
School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance |
document_store_str |
1 |
active_str |
0 |
description |
Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision-makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data-driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short-term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data-forecasting model-decision support” decision paradigm for real-world problems. |
published_date |
2024-08-15T16:26:29Z |
_version_ |
1808736126798659584 |
score |
11.033506 |