酱香型白酒(以下简称“酱酒”)作为中国白酒三大基础香型之一,以贵州茅台酒为典型代表,以其独特的香气与口感在市场中享有盛誉。酱酒产业不仅是贵州省的重要经济支柱,更是区域经济高质量发展的关键领域。为进一步推动该产业的发展,贵州...酱香型白酒(以下简称“酱酒”)作为中国白酒三大基础香型之一,以贵州茅台酒为典型代表,以其独特的香气与口感在市场中享有盛誉。酱酒产业不仅是贵州省的重要经济支柱,更是区域经济高质量发展的关键领域。为进一步推动该产业的发展,贵州省政府在2022年明确将酱酒产业高质量发展列入重点工作。然而,酱酒市场的扩张面临诸多挑战,其中,电商平台的流量聚集化问题尤为突出。针对贵州省酱酒在电商平台的消费者评论数据,本文拟通过数据分析方法对市场痛点进行深入探讨。首先,采用决策树模型对文本评论数据进行情感分类,识别消费者的情感倾向,提炼消费者的潜在需求与偏好,为产品改进提供数据支持。其次,基于潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)模型,对评论数据的主题词分布进行建模与分析,以揭示评论的核心关注点与潜在主题。结合正向与负向评价结果,本文将提出针对性的产品优化与推广策略,为酱酒产业在电商平台的可持续发展提供科学依据。此研究旨在通过情感分析与主题建模技术,为酱酒企业精准把握市场需求与消费者偏好提供实证支持,助力酱酒产业高质量发展与品牌建设。As one of the three basic aroma types of Chinese liquor, Moutai in Guizhou Province is a typical representative of soy saucetype baijiu (from now on referred to as “soy sauce liquor”), which enjoys a high reputation in the market for its unique aroma and taste. The soy sauce wine industry is not only an important economic pillar of Guizhou Province but also a key area for the high-quality development of the regional economy. To further promote the development of this industry, the Guizhou provincial government has explicitly included the high-quality development of the soy sauce wine industry among its priorities in 2022. However, the expansion of the soy sauce wine market faces many challenges, among which is the problem of traffic aggregation on e-commerce platforms, which is particularly prominent. Aiming at the consumer review data of soy sauce wine in Guizhou province on e-commerce platforms, this paper intends to conduct an in-depth discussion of the market pain points through data analysis methods. First, a decision tree model is used to emotionally classify the text comment data, identify consumers’ emotional tendencies, and refine consumers’ potential needs and preferences to provide data support for product improvement, and second, based on the Latent Dirichlet Allocation (LDA) model, the topic word distribution of review data, is modeled and analyzed to reveal the core concerns and potential topics of reviews. Combining the positive and negative evaluation results, this paper will propose targeted product optimization and promotion strategies to provide a scientific basis for the sustainable development of the soy sauce wine industry on e-commerce platforms. This study aims to provide empirical support for soy sauce wine enterprises to accurately grasp the market demand and consumer preferences through sentiment analysis and theme modeling technology and help the soy sauce wine industry to develop high quality and brand building.展开更多
准确预测电子商务销售额对电子商务业高质量发展具有重大意义。本文以贵州省为例,基于收集到的5个预测指标,建立可加模型对贵州省电子商务销售额未来三年进行预测。首先使用变分自编码器(VAE)方法对2003~2012年电子商务销售额缺失值进...准确预测电子商务销售额对电子商务业高质量发展具有重大意义。本文以贵州省为例,基于收集到的5个预测指标,建立可加模型对贵州省电子商务销售额未来三年进行预测。首先使用变分自编码器(VAE)方法对2003~2012年电子商务销售额缺失值进行估计补充,然后使用互信息评估5个预测指标被选择的合理性,同时使用基于B样条的核密度回归估计方法对可加模型进行估计。最后使用GM (1, 1)模型预测未来三年5个预测指标值,然后带入到已建立的可加模型中,从而得到电子商务销售额未来三年的预测值。Accurate prediction of e-commerce sales is of significant importance for the high-quality development of the e-commerce industry. Taking Guizhou Province as an example, this paper establishes an additive model to forecast the e-commerce sales in Guizhou Province for the next three years based on five collected predictive indicators. Firstly, the Variational Autoencoder (VAE) method is used to estimate and supplement the missing values of e-commerce sales from 2003 to 2012. Then, the mutual information is utilized to assess the rationality of the five predictive indicators selected, while the B-spline-based kernel density regression estimation method is employed to estimate the additive model. Finally, the GM (1, 1) model is used to predict the values of the five predictive indicators for the next three years, which are then incorporated into the established additive model to obtain the forecasted e-commerce sales for the next three years.展开更多
产业振兴是乡村振兴的重中之重,要立足特色资源,发展优势产业。而抹茶作为茶产业发展的新兴之秀,因产品附加值高、市场前景广阔等优势引起了铜仁市政府的高度关注。本文主要以抹茶消费者为主体,采用配额抽样和方便抽样相结合的调查方法...产业振兴是乡村振兴的重中之重,要立足特色资源,发展优势产业。而抹茶作为茶产业发展的新兴之秀,因产品附加值高、市场前景广阔等优势引起了铜仁市政府的高度关注。本文主要以抹茶消费者为主体,采用配额抽样和方便抽样相结合的调查方法,利用网络问卷为调查手段进行调查。运用K-Means聚类算法构建消费者画像,总结得出相应结论:(1) 消费者层面:年轻消费者更乐意购买抹茶产品,女性消费者远多于男性消费者;(2) 企业层面:中小微企业对贵茶联盟的依赖性很强,抹茶行业缺乏自主发展的动力;产品应用面窄,利润低;品牌知名度不足,市场推广难度大;(3) 体系层面:抹茶产业相关标准体系尚不完善,对于抹茶的种植、关键环节缺乏统一的标准,影响区域合作发展和产业整体发展。Industrial revitalization is the cornerstone of rural revitalization, urging the development of advantageous industries based on unique local resources. As an emerging star in the tea industry, matcha has garnered significant attention from the Tongren municipal government due to its high added value and broad market prospects. This paper primarily focuses on matcha consumers, using a combination of quota sampling and convenience sampling methods, and conducts surveys through online questionnaires. Utilizing K-Means clustering to create consumer profiles, the following conclusions are drawn: (1) Consumer Level: Young consumers are more inclined to purchase matcha products, with female consumers significantly outnumbering male consumers. (2) Enterprise Leve: Small and medium-sized enterprises (SMEs) heavily rely on the Gui Tea Alliance, leading to a lack of autonomous development within the matcha industry. The product application scope is narrow, profits are low, and brand awareness is insufficient, making market promotion challenging. (3) System Level: The standardization system related to the matcha industry is still underdeveloped, with a lack of unified standards for key processes such as matcha cultivation. This hampers regional cooperation and the overall development of the industry.展开更多
随着数字技术的不断发展和普及数字经济得到了迅速发展,对城市的发展带来了广阔的机遇。发展潜力分析对了解一个区域城市自身的产业发展规律和协助政府经济决策都具有相当重要的研究意义。我们可通过对比分析中国某省内在数字经济背景...随着数字技术的不断发展和普及数字经济得到了迅速发展,对城市的发展带来了广阔的机遇。发展潜力分析对了解一个区域城市自身的产业发展规律和协助政府经济决策都具有相当重要的研究意义。我们可通过对比分析中国某省内在数字经济背景下的其它各个主要城市及其经济发展及其潜力,做出哪些对其各大城市发展最为有利的区域发展的计划方案以及具体决策,从而使之发展得到进一步的提高。以贵州省9个市为例,采用主成分分析法,得出各市县的经济综合价值。利于综合制定产业发展与对策,以期能够为贵州省近期的发展经济战略进一步制定以及区域间协调发展提供参考。With the continuous development and popularization of digital technology, the digital economy has developed rapidly, bringing vast opportunities to the development of cities. The analysis of development potential is of great research significance for understanding the industrial development laws of a regional city itself and assisting the government in economic decision-making. We can compare and analyze the economic development and potential of other major cities in a certain province of China under the background of digital economy, and make specific plans and decisions that are most beneficial for the development of its major cities, so as to further improve their development. Taking nine cities in Guizhou Province as an example, using principal component analysis, we can obtain the comprehensive economic value of each city and county, which is conducive to the comprehensive formulation of industrial development and countermeasures. This is hoped to provide reference for the further formulation of economic development strategies and coordinated development between regions in Guizhou Province in the near future.展开更多
在经济快速发展和金融市场波动的背景下,股价预测对于投资者、企业管理者以及金融机构来说至关重要。这不仅有助于他们掌握未来的风险,还为投资决策和监管提供了重要依据。单一预测模型在股价预测中很难同时捕获到数据序列中的线性和非...在经济快速发展和金融市场波动的背景下,股价预测对于投资者、企业管理者以及金融机构来说至关重要。这不仅有助于他们掌握未来的风险,还为投资决策和监管提供了重要依据。单一预测模型在股价预测中很难同时捕获到数据序列中的线性和非线性特征,因此预测效果不理想。针对该问题提出了一种基于ARIMA模型与LSTM模型相结合的股价预测模型,综合考虑线性与非线性特征的股价预测。本文采用贵州茅台(600519) 2018年1月2日到2023年12月29日之间的每个交易日的日收盘价进行实验,实验结果表明,与单一的ARIMA模型和LSTM模型相比,ARIMA-LSTM组合模型在股价预测方面取得了较好的效果。In the context of rapid economic development and fluctuating financial markets, stock price prediction is crucial for investors, corporate managers, and financial institutions. It not only helps them assess future risks but also provides essential support for investment decisions and regulatory actions. A single predictive model often struggles to capture both linear and nonlinear features in stock price data, leading to suboptimal forecasting results. To address this issue, a hybrid stock price prediction model combining the ARIMA model and the LSTM model is proposed, which comprehensively considers both linear and nonlinear characteristics. This study uses the daily closing prices of Kweichow Moutai (600519) from January 2, 2018, to December 29, 2023, for experiments. The experimental results show that the ARIMA-LSTM hybrid model achieves better stock price prediction performance compared to using either the ARIMA or LSTM model alone.展开更多
在传统的雪堆博弈中,通常假设对所有背叛者施加惩罚。然而,在实际的市场营销环境中,企业的惩罚预算通常是有限的,因此考虑随机选择一定比例的背叛顾客进行惩罚显得尤为重要。在此背景下,我们探讨将分段惩罚机制引入多人雪堆博弈模型,重...在传统的雪堆博弈中,通常假设对所有背叛者施加惩罚。然而,在实际的市场营销环境中,企业的惩罚预算通常是有限的,因此考虑随机选择一定比例的背叛顾客进行惩罚显得尤为重要。在此背景下,我们探讨将分段惩罚机制引入多人雪堆博弈模型,重点分析其对合作演化的影响。我们的目的是研究分段惩罚机制在雪堆博弈中的应用,分析均衡点的确定性稳定和随机稳定。研究结果表明,在确定性情况下,无论是二人还是多人雪堆博弈中,合作者的比例随着惩罚比例的增加而提高,从而更有效地促进市场中的合作与品牌忠诚。这一发现为企业设计灵活的惩罚策略提供了理论支持,有助于在资源受限的情况下优化客户关系管理,提升电子商务平台的信任度和用户满意度。In the traditional snowdrift game, it is usually assumed that all betrayers are punished. However, in the actual marketing environment, the punishment budget of enterprises is usually limited, so it is particularly important to consider randomly selecting a certain proportion of betrayed customers for punishment. In this context, we explore the introduction of the segmented punishment mechanism into the multi-person snowdrift game model, focusing on its impact on the evolution of cooperation. Our purpose is to study the application of piecewise penalty mechanism in snowdrift game, and analyze the deterministic stability and stochastic stability of the equilibrium point. The results show that in the deterministic case, whether it is a two-person or multi-person snowdrift game, the proportion of collaborators increases with the increase of the penalty ratio, thus more effectively promoting cooperation and brand loyalty in the market. This finding provides theoretical support for enterprises to design flexible punishment strategies, helps to optimize customer relationship management in resource-constrained situations, and improves the trust and user satisfaction of e-commerce platforms.展开更多
图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高...图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高预测精度与推荐效果。在电商平台中,商品间的复杂关系往往通过图结构表示,其中节点代表商品,边代表商品间的关联或共同特征。通过优化图的构建和学习方法,能够更准确地捕捉商品之间的潜在联系,从而提升链接预测的准确性和推荐质量。优化后的图结构可以帮助算法更好地处理大规模商品数据,增强模型的泛化能力,进而提升电商平台的个性化推荐系统,增加用户购买的可能性,并促进销售增长。本文提出了一种新的稀疏正则化与图结构学习模型搜索方法(SGSL)。通过引入边缘修剪的正则化项等技术,SGSL能够在节点不变分类任务中显著提高性能,同时减少在节点变化任务中搜索到错误边的风险。实验表明,SGSL能有效增强图神经网络模型的性能。Graph Structure Learning (GSL) enhances the representational capacity and performance of graphs by optimizing their structure. GSL better captures the relationships between nodes in graph data, which facilitates more effective information propagation. The application of graph structure optimization in product link prediction aims to improve prediction accuracy and recommendation performance by refining the graph structure that represents the relationships between products. In e-commerce platforms, the complex relationships between products are often represented through graph structures, where nodes represent products and edges represent associations or shared features. By optimizing graph construction and learning methods, the underlying relationships between products can be more accurately captured, thereby improving link prediction accuracy and recommendation quality. The optimized graph structure helps algorithms better handle large-scale product data, enhancing the model’s generalization ability, which in turn improves personalized recommendation systems, increases the likelihood of user purchases, and drives sales growth. This paper introduces a novel Sparse Regularization and Graph Structure Learning Model Search method (SGSL). By incorporating techniques such as edge pruning regularization, SGSL significantly improves performance in node-invariant classification tasks while reducing the risk of selecting incorrect edges in node-variant tasks. Experimental results show that SGSL effectively enhances the performance of graph neural network models.展开更多
本文探讨了双碳政策实施背景下,中国能源消耗结构转型与国际贸易的相互影响。随着全球对可持续发展的重视,中国正加速向可再生能源转型,逐步降低对化石燃料的依赖。这一转型不仅改善了国内能源安全,也重塑了国际能源贸易格局。文章基于...本文探讨了双碳政策实施背景下,中国能源消耗结构转型与国际贸易的相互影响。随着全球对可持续发展的重视,中国正加速向可再生能源转型,逐步降低对化石燃料的依赖。这一转型不仅改善了国内能源安全,也重塑了国际能源贸易格局。文章基于线性回归模型预测了中国的碳排放量,并分析了近十年来的能源消耗模式和结构变化。同时,通过相关性分析揭示了能源消耗与国际贸易之间的内在联系,指出两者在全球绿色经济中的互动关系。中国的转型不仅有助于改善环境状况,还为国际市场带来了新的机遇与挑战。随着可再生能源比重的提升,中国在全球能源供应链中的地位也在发生变化。这一变化促使中国企业在技术创新和国际合作方面不断提升,从而推动国际贸易向更高标准和更环保的方向发展。总的来说,本文为理解中国在全球能源转型中的角色提供了重要洞见,强调了可持续发展与国际贸易之间的密切联系。This paper examines the relationship between the structural transformation of China’s energy consumption and international trade, with a particular focus on the implementation of peak carbon and carbon-neutral policy policies. In light of the global emphasis on sustainable development, China is accelerating its transition to renewable energy sources and gradually reducing its dependence on fossil fuels. This transition has the additional benefit of enhancing domestic energy security while simultaneously influencing the configuration of international energy trade patterns. The article employs a linear regression model to predict China’s carbon emissions and conducts an in-depth analysis of the shifts in energy consumption patterns and structures that have occurred over the past decade. Concurrently, correlation analyses elucidate the intrinsic interconnections between energy consumption and international trade, underscoring the interplay between the two in the global green economy. China’s transition not only has positive implications for environmental conditions, but also presents new opportunities and challenges for the international market. As the proportion of renewable energy sources increases, China’s role in the global energy supply chain is evolving. This shift has prompted Chinese companies to enhance their technological innovation and international collaboration, thereby propelling international trade towards higher standards and a more environmentally conscious approach. Overall, this paper offers valuable insights into China’s role in the global energy transition, underscoring the intrinsic link between sustainable development and international trade.展开更多
文摘酱香型白酒(以下简称“酱酒”)作为中国白酒三大基础香型之一,以贵州茅台酒为典型代表,以其独特的香气与口感在市场中享有盛誉。酱酒产业不仅是贵州省的重要经济支柱,更是区域经济高质量发展的关键领域。为进一步推动该产业的发展,贵州省政府在2022年明确将酱酒产业高质量发展列入重点工作。然而,酱酒市场的扩张面临诸多挑战,其中,电商平台的流量聚集化问题尤为突出。针对贵州省酱酒在电商平台的消费者评论数据,本文拟通过数据分析方法对市场痛点进行深入探讨。首先,采用决策树模型对文本评论数据进行情感分类,识别消费者的情感倾向,提炼消费者的潜在需求与偏好,为产品改进提供数据支持。其次,基于潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)模型,对评论数据的主题词分布进行建模与分析,以揭示评论的核心关注点与潜在主题。结合正向与负向评价结果,本文将提出针对性的产品优化与推广策略,为酱酒产业在电商平台的可持续发展提供科学依据。此研究旨在通过情感分析与主题建模技术,为酱酒企业精准把握市场需求与消费者偏好提供实证支持,助力酱酒产业高质量发展与品牌建设。As one of the three basic aroma types of Chinese liquor, Moutai in Guizhou Province is a typical representative of soy saucetype baijiu (from now on referred to as “soy sauce liquor”), which enjoys a high reputation in the market for its unique aroma and taste. The soy sauce wine industry is not only an important economic pillar of Guizhou Province but also a key area for the high-quality development of the regional economy. To further promote the development of this industry, the Guizhou provincial government has explicitly included the high-quality development of the soy sauce wine industry among its priorities in 2022. However, the expansion of the soy sauce wine market faces many challenges, among which is the problem of traffic aggregation on e-commerce platforms, which is particularly prominent. Aiming at the consumer review data of soy sauce wine in Guizhou province on e-commerce platforms, this paper intends to conduct an in-depth discussion of the market pain points through data analysis methods. First, a decision tree model is used to emotionally classify the text comment data, identify consumers’ emotional tendencies, and refine consumers’ potential needs and preferences to provide data support for product improvement, and second, based on the Latent Dirichlet Allocation (LDA) model, the topic word distribution of review data, is modeled and analyzed to reveal the core concerns and potential topics of reviews. Combining the positive and negative evaluation results, this paper will propose targeted product optimization and promotion strategies to provide a scientific basis for the sustainable development of the soy sauce wine industry on e-commerce platforms. This study aims to provide empirical support for soy sauce wine enterprises to accurately grasp the market demand and consumer preferences through sentiment analysis and theme modeling technology and help the soy sauce wine industry to develop high quality and brand building.
文摘准确预测电子商务销售额对电子商务业高质量发展具有重大意义。本文以贵州省为例,基于收集到的5个预测指标,建立可加模型对贵州省电子商务销售额未来三年进行预测。首先使用变分自编码器(VAE)方法对2003~2012年电子商务销售额缺失值进行估计补充,然后使用互信息评估5个预测指标被选择的合理性,同时使用基于B样条的核密度回归估计方法对可加模型进行估计。最后使用GM (1, 1)模型预测未来三年5个预测指标值,然后带入到已建立的可加模型中,从而得到电子商务销售额未来三年的预测值。Accurate prediction of e-commerce sales is of significant importance for the high-quality development of the e-commerce industry. Taking Guizhou Province as an example, this paper establishes an additive model to forecast the e-commerce sales in Guizhou Province for the next three years based on five collected predictive indicators. Firstly, the Variational Autoencoder (VAE) method is used to estimate and supplement the missing values of e-commerce sales from 2003 to 2012. Then, the mutual information is utilized to assess the rationality of the five predictive indicators selected, while the B-spline-based kernel density regression estimation method is employed to estimate the additive model. Finally, the GM (1, 1) model is used to predict the values of the five predictive indicators for the next three years, which are then incorporated into the established additive model to obtain the forecasted e-commerce sales for the next three years.
文摘产业振兴是乡村振兴的重中之重,要立足特色资源,发展优势产业。而抹茶作为茶产业发展的新兴之秀,因产品附加值高、市场前景广阔等优势引起了铜仁市政府的高度关注。本文主要以抹茶消费者为主体,采用配额抽样和方便抽样相结合的调查方法,利用网络问卷为调查手段进行调查。运用K-Means聚类算法构建消费者画像,总结得出相应结论:(1) 消费者层面:年轻消费者更乐意购买抹茶产品,女性消费者远多于男性消费者;(2) 企业层面:中小微企业对贵茶联盟的依赖性很强,抹茶行业缺乏自主发展的动力;产品应用面窄,利润低;品牌知名度不足,市场推广难度大;(3) 体系层面:抹茶产业相关标准体系尚不完善,对于抹茶的种植、关键环节缺乏统一的标准,影响区域合作发展和产业整体发展。Industrial revitalization is the cornerstone of rural revitalization, urging the development of advantageous industries based on unique local resources. As an emerging star in the tea industry, matcha has garnered significant attention from the Tongren municipal government due to its high added value and broad market prospects. This paper primarily focuses on matcha consumers, using a combination of quota sampling and convenience sampling methods, and conducts surveys through online questionnaires. Utilizing K-Means clustering to create consumer profiles, the following conclusions are drawn: (1) Consumer Level: Young consumers are more inclined to purchase matcha products, with female consumers significantly outnumbering male consumers. (2) Enterprise Leve: Small and medium-sized enterprises (SMEs) heavily rely on the Gui Tea Alliance, leading to a lack of autonomous development within the matcha industry. The product application scope is narrow, profits are low, and brand awareness is insufficient, making market promotion challenging. (3) System Level: The standardization system related to the matcha industry is still underdeveloped, with a lack of unified standards for key processes such as matcha cultivation. This hampers regional cooperation and the overall development of the industry.
文摘随着数字技术的不断发展和普及数字经济得到了迅速发展,对城市的发展带来了广阔的机遇。发展潜力分析对了解一个区域城市自身的产业发展规律和协助政府经济决策都具有相当重要的研究意义。我们可通过对比分析中国某省内在数字经济背景下的其它各个主要城市及其经济发展及其潜力,做出哪些对其各大城市发展最为有利的区域发展的计划方案以及具体决策,从而使之发展得到进一步的提高。以贵州省9个市为例,采用主成分分析法,得出各市县的经济综合价值。利于综合制定产业发展与对策,以期能够为贵州省近期的发展经济战略进一步制定以及区域间协调发展提供参考。With the continuous development and popularization of digital technology, the digital economy has developed rapidly, bringing vast opportunities to the development of cities. The analysis of development potential is of great research significance for understanding the industrial development laws of a regional city itself and assisting the government in economic decision-making. We can compare and analyze the economic development and potential of other major cities in a certain province of China under the background of digital economy, and make specific plans and decisions that are most beneficial for the development of its major cities, so as to further improve their development. Taking nine cities in Guizhou Province as an example, using principal component analysis, we can obtain the comprehensive economic value of each city and county, which is conducive to the comprehensive formulation of industrial development and countermeasures. This is hoped to provide reference for the further formulation of economic development strategies and coordinated development between regions in Guizhou Province in the near future.
文摘在经济快速发展和金融市场波动的背景下,股价预测对于投资者、企业管理者以及金融机构来说至关重要。这不仅有助于他们掌握未来的风险,还为投资决策和监管提供了重要依据。单一预测模型在股价预测中很难同时捕获到数据序列中的线性和非线性特征,因此预测效果不理想。针对该问题提出了一种基于ARIMA模型与LSTM模型相结合的股价预测模型,综合考虑线性与非线性特征的股价预测。本文采用贵州茅台(600519) 2018年1月2日到2023年12月29日之间的每个交易日的日收盘价进行实验,实验结果表明,与单一的ARIMA模型和LSTM模型相比,ARIMA-LSTM组合模型在股价预测方面取得了较好的效果。In the context of rapid economic development and fluctuating financial markets, stock price prediction is crucial for investors, corporate managers, and financial institutions. It not only helps them assess future risks but also provides essential support for investment decisions and regulatory actions. A single predictive model often struggles to capture both linear and nonlinear features in stock price data, leading to suboptimal forecasting results. To address this issue, a hybrid stock price prediction model combining the ARIMA model and the LSTM model is proposed, which comprehensively considers both linear and nonlinear characteristics. This study uses the daily closing prices of Kweichow Moutai (600519) from January 2, 2018, to December 29, 2023, for experiments. The experimental results show that the ARIMA-LSTM hybrid model achieves better stock price prediction performance compared to using either the ARIMA or LSTM model alone.
文摘在传统的雪堆博弈中,通常假设对所有背叛者施加惩罚。然而,在实际的市场营销环境中,企业的惩罚预算通常是有限的,因此考虑随机选择一定比例的背叛顾客进行惩罚显得尤为重要。在此背景下,我们探讨将分段惩罚机制引入多人雪堆博弈模型,重点分析其对合作演化的影响。我们的目的是研究分段惩罚机制在雪堆博弈中的应用,分析均衡点的确定性稳定和随机稳定。研究结果表明,在确定性情况下,无论是二人还是多人雪堆博弈中,合作者的比例随着惩罚比例的增加而提高,从而更有效地促进市场中的合作与品牌忠诚。这一发现为企业设计灵活的惩罚策略提供了理论支持,有助于在资源受限的情况下优化客户关系管理,提升电子商务平台的信任度和用户满意度。In the traditional snowdrift game, it is usually assumed that all betrayers are punished. However, in the actual marketing environment, the punishment budget of enterprises is usually limited, so it is particularly important to consider randomly selecting a certain proportion of betrayed customers for punishment. In this context, we explore the introduction of the segmented punishment mechanism into the multi-person snowdrift game model, focusing on its impact on the evolution of cooperation. Our purpose is to study the application of piecewise penalty mechanism in snowdrift game, and analyze the deterministic stability and stochastic stability of the equilibrium point. The results show that in the deterministic case, whether it is a two-person or multi-person snowdrift game, the proportion of collaborators increases with the increase of the penalty ratio, thus more effectively promoting cooperation and brand loyalty in the market. This finding provides theoretical support for enterprises to design flexible punishment strategies, helps to optimize customer relationship management in resource-constrained situations, and improves the trust and user satisfaction of e-commerce platforms.
文摘图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高预测精度与推荐效果。在电商平台中,商品间的复杂关系往往通过图结构表示,其中节点代表商品,边代表商品间的关联或共同特征。通过优化图的构建和学习方法,能够更准确地捕捉商品之间的潜在联系,从而提升链接预测的准确性和推荐质量。优化后的图结构可以帮助算法更好地处理大规模商品数据,增强模型的泛化能力,进而提升电商平台的个性化推荐系统,增加用户购买的可能性,并促进销售增长。本文提出了一种新的稀疏正则化与图结构学习模型搜索方法(SGSL)。通过引入边缘修剪的正则化项等技术,SGSL能够在节点不变分类任务中显著提高性能,同时减少在节点变化任务中搜索到错误边的风险。实验表明,SGSL能有效增强图神经网络模型的性能。Graph Structure Learning (GSL) enhances the representational capacity and performance of graphs by optimizing their structure. GSL better captures the relationships between nodes in graph data, which facilitates more effective information propagation. The application of graph structure optimization in product link prediction aims to improve prediction accuracy and recommendation performance by refining the graph structure that represents the relationships between products. In e-commerce platforms, the complex relationships between products are often represented through graph structures, where nodes represent products and edges represent associations or shared features. By optimizing graph construction and learning methods, the underlying relationships between products can be more accurately captured, thereby improving link prediction accuracy and recommendation quality. The optimized graph structure helps algorithms better handle large-scale product data, enhancing the model’s generalization ability, which in turn improves personalized recommendation systems, increases the likelihood of user purchases, and drives sales growth. This paper introduces a novel Sparse Regularization and Graph Structure Learning Model Search method (SGSL). By incorporating techniques such as edge pruning regularization, SGSL significantly improves performance in node-invariant classification tasks while reducing the risk of selecting incorrect edges in node-variant tasks. Experimental results show that SGSL effectively enhances the performance of graph neural network models.
文摘本文探讨了双碳政策实施背景下,中国能源消耗结构转型与国际贸易的相互影响。随着全球对可持续发展的重视,中国正加速向可再生能源转型,逐步降低对化石燃料的依赖。这一转型不仅改善了国内能源安全,也重塑了国际能源贸易格局。文章基于线性回归模型预测了中国的碳排放量,并分析了近十年来的能源消耗模式和结构变化。同时,通过相关性分析揭示了能源消耗与国际贸易之间的内在联系,指出两者在全球绿色经济中的互动关系。中国的转型不仅有助于改善环境状况,还为国际市场带来了新的机遇与挑战。随着可再生能源比重的提升,中国在全球能源供应链中的地位也在发生变化。这一变化促使中国企业在技术创新和国际合作方面不断提升,从而推动国际贸易向更高标准和更环保的方向发展。总的来说,本文为理解中国在全球能源转型中的角色提供了重要洞见,强调了可持续发展与国际贸易之间的密切联系。This paper examines the relationship between the structural transformation of China’s energy consumption and international trade, with a particular focus on the implementation of peak carbon and carbon-neutral policy policies. In light of the global emphasis on sustainable development, China is accelerating its transition to renewable energy sources and gradually reducing its dependence on fossil fuels. This transition has the additional benefit of enhancing domestic energy security while simultaneously influencing the configuration of international energy trade patterns. The article employs a linear regression model to predict China’s carbon emissions and conducts an in-depth analysis of the shifts in energy consumption patterns and structures that have occurred over the past decade. Concurrently, correlation analyses elucidate the intrinsic interconnections between energy consumption and international trade, underscoring the interplay between the two in the global green economy. China’s transition not only has positive implications for environmental conditions, but also presents new opportunities and challenges for the international market. As the proportion of renewable energy sources increases, China’s role in the global energy supply chain is evolving. This shift has prompted Chinese companies to enhance their technological innovation and international collaboration, thereby propelling international trade towards higher standards and a more environmentally conscious approach. Overall, this paper offers valuable insights into China’s role in the global energy transition, underscoring the intrinsic link between sustainable development and international trade.
