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
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
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.
