股票市场预测--基于词典方法的 COVID-19 方案

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Web Intelligence Pub Date : 2023-12-01 DOI:10.3233/web-230092
Y. Ayyappa, A.P. Siva Kumar
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引用次数: 0

摘要

股票市场预测由于其难以置信的随机性,一直是经济学领域的一个难题。这种专家系统的创建有助于投资者对某一公司做出投资决策。由于股票市场的复杂性,使用单一数据源不足以准确反映影响股票波动的所有变量。然而,预测股市走势是一项具有挑战性的工作,需要大量的数据分析,尤其是从大数据的角度。为了解决这些问题并产生可行的解决方案,需要适当的统计模型和人工智能算法。本文旨在通过以下四个阶段提出一种新的股票市场预测方法:它们是预处理、特征提取、改进的特征级融合和预测。输入的数据首先要经过一个准备步骤,在这个步骤中处理股票、新闻和Twitter数据(与COVID-19疫情有关)。在大数据视角下,输入数据被考虑在内。然后将这些预处理过的数据进行特征提取,本例中改进的基于方面的词典生成、PMI和基于n-gram的特征来自新闻和Twitter数据,而基于技术指标的特征来自股票数据。然后将改进的特征级融合阶段应用于提取的特征。在预测阶段提出了包括DBN、CNN和DRN在内的集成分类器。此外,还提出了一种SI-MRFO模型,通过调整最佳分类器权重来提高预测模型的效率。最后,对比了SI-MRFO模型在MAE、MAPE、MSE和MSLE方面与现有模型的有效性。SI-MRFO在第90个学习百分比的最小MAE率约为0.015,而其他模型获得最大评级。
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Stock market prediction-COVID-19 scenario with lexicon-based approach
Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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