利用可解释的人工智能和大数据建立棉纱期货价格波动的新概率预测模型

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-08-15 DOI:10.1002/for.3185
Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang, Mohammad Zoynul Abedin
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引用次数: 0

摘要

棉花、棉纱等棉纺产品价格波动频繁,增加了行业参与者制定合理经营决策方案的难度。为了支持棉纺织行业的决策者,我们运用数据挖掘方法,从大数据中提取影响棉纱期货价格的主要影响因素,并建立一个带有不确定性评估的棉纱价格波动概率预测模型。基于可解释人工智能(XAI)和数据驱动的视角,我们使用 LassoNet 算法从海量数据中提取与目标变量最相关的 18 个特征,并将所选特征的重要性值可视化,以提高可靠性。此外,通过将保形预测(CP)与量子回归(QR)相结合,应用长短期记忆(LSTM)模型点估计结果的不确定性度量来提高模型的应用价值。最后,引入 SHAP(SHapley Additive exPlanations),分析输入特征的 SHAP 值对输出结果的影响,深入探讨输入特征与目标变量之间的相互作用和作用机制,提高模型的可解释性。我们的模型为现实问题提供了一种 "大数据-预测模型-决策支持 "的决策范式。
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A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data
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.
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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