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A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data

Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang Orcid Logo, Mohammad Abedin Orcid Logo

Journal of Forecasting, Volume: 44, Issue: 1, Pages: 112 - 135

Swansea University Author: Mohammad Abedin Orcid Logo

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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...

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Published in: Journal of Forecasting
ISSN: 0277-6693 1099-131X
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa67179
first_indexed 2024-07-25T10:34:05Z
last_indexed 2025-01-09T20:30:09Z
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spelling 2024-12-13T14:42:57.5639767 v2 67179 2024-07-25 A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 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 44 1 112 135 Wiley 0277-6693 1099-131X Big data mining, forecasting, probabilistic modelling, XAI 1 1 2025 2025-01-01 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, 71871172 2024-12-13T14:42:57.5639767 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 0000-0002-4688-0619 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 44
container_issue 1
container_start_page 112
publishDate 2025
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 2025-01-01T14:38:00Z
_version_ 1821960233787850752
score 11.048149

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