探索用于股市预测的递归神经网络方法的不同动态 - 对比研究

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Artificial Intelligence Pub Date : 2024-06-24 DOI:10.1080/08839514.2024.2371706

Ajit Mohan Pattanayak, Aleena Swetapadma, Biswajit Sahoo

引用次数: 0

摘要

股票市场错综复杂、难以预测，这凸显了精确预测对于及时发现股市下滑和随后反弹的重要性。各种因素，包括新闻、股票价格、股票价格波动、股票价格的波动、股票价格的波动、股票价格的波动、股票价格的波动、股票价格的波动、股票价格的波动、股票价格的波动...

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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Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study

The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news,...

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来源期刊

Applied Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

5.20

自引率

3.60%

发文量

106

审稿时长

6 months

期刊介绍： Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.