Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-02-01 Epub Date: 2023-11-17 DOI:10.1089/big.2022.0287
Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang
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引用次数: 0

Abstract

Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.

基于可解释人工智能的棉花价格指数波动风险的长短期记忆模型。
市场的不确定性极大地干扰了市场参与者的决策和计划,从而增加了决策的风险,导致决策者的利益受损。棉花价格指数(以下简称棉价)波动具有高度的噪声、非线性和随机性,易受供需、气候、代用品等政策因素的影响,具有较大的不确定性。为了降低决策风险,为决策者提供决策支持,本文在已有研究的基础上,将影响棉花价格指数波动的13个因素进行整合,并进一步划分为交易数据和交互数据。构建了长短期记忆(LSTM)模型,并对棉花价格指数波动进行了对比实验分析。为了使构建的模型具有可解释性,我们使用可解释性人工智能(XAI)技术对输入特征进行统计分析。实验结果表明,LSTM模型能准确分析棉花价格指数波动趋势,但不能准确预测棉花实际价格;交易数据加交互数据在分析棉花价格波动趋势时比交易数据更敏感,可以对棉花价格波动分析产生积极的影响。本研究可以准确反映棉花市场的波动趋势,为国家、企业和棉农决策提供参考,降低棉花价格频繁波动带来的风险。使用XAI技术对模型进行分析,建立决策者对模型的信心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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