A new multivariate decomposition-ensemble approach with denoised neighborhood rough set for stock price forecasting over time-series information system

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2025-01-09 DOI:10.1007/s10489-024-06070-0
Juncheng Bai, Bingzhen Sun, Yuqi Guo, Xiaoli Chu
{"title":"A new multivariate decomposition-ensemble approach with denoised neighborhood rough set for stock price forecasting over time-series information system","authors":"Juncheng Bai,&nbsp;Bingzhen Sun,&nbsp;Yuqi Guo,&nbsp;Xiaoli Chu","doi":"10.1007/s10489-024-06070-0","DOIUrl":null,"url":null,"abstract":"<div><p>The uncertainty of the stock market is the foundation for investors to obtain returns. Driven by interests, stock price forecasting has become a research hotspot. However, as the high latitude, highly volatile, and noisy, forecasting the stock prices has become a highly challenging task. The existing stock price forecasting methods only study low latitude data, which is unable to reflect the cumulative effect of multiple factors on stock price. To effectively address the high latitude, high volatility, and noise of stock price, a time-series information system (TSIS) forecasting approach for stock price is proposed. Aiming at dynamically depicting the real-world decision-making scenarios from a finer granularity, the TSIS is constructed based on the information systems. Then, a denoised neighborhood rough set (DNRS) model based on the TSIS is proposed by local density factor to achieve the purpose of feature selection, which can weaken the impact of noise on sample data. Subsequently, the multivariate empirical mode decomposition (MEMD) and multivariate kernel extreme learning machine (MKELM) are employed to decompose and forecast. Finally, the proposed TSIS forecasting approach is applied to stock price. Experimental results show that the TSIS forecasting approach for stock price has excellent performance and can be provided in the quantitative trading of stock market.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 4","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-024-06070-0","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The uncertainty of the stock market is the foundation for investors to obtain returns. Driven by interests, stock price forecasting has become a research hotspot. However, as the high latitude, highly volatile, and noisy, forecasting the stock prices has become a highly challenging task. The existing stock price forecasting methods only study low latitude data, which is unable to reflect the cumulative effect of multiple factors on stock price. To effectively address the high latitude, high volatility, and noise of stock price, a time-series information system (TSIS) forecasting approach for stock price is proposed. Aiming at dynamically depicting the real-world decision-making scenarios from a finer granularity, the TSIS is constructed based on the information systems. Then, a denoised neighborhood rough set (DNRS) model based on the TSIS is proposed by local density factor to achieve the purpose of feature selection, which can weaken the impact of noise on sample data. Subsequently, the multivariate empirical mode decomposition (MEMD) and multivariate kernel extreme learning machine (MKELM) are employed to decompose and forecast. Finally, the proposed TSIS forecasting approach is applied to stock price. Experimental results show that the TSIS forecasting approach for stock price has excellent performance and can be provided in the quantitative trading of stock market.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于去噪邻域粗糙集的多变量分解集成方法用于时间序列信息系统股票价格预测
股票市场的不确定性是投资者获得收益的基础。在利益的驱动下,股价预测已成为一个研究热点。然而,由于股票市场纬度高、波动大、噪声大,股票价格预测成为一项极具挑战性的任务。现有的股价预测方法只研究低纬度数据,无法反映多因素对股价的累积效应。为了有效地解决股票价格的高纬度、高波动性和噪声问题,提出了一种时间序列信息系统(TSIS)股票价格预测方法。为了从更细的粒度上动态地描述现实世界的决策场景,基于信息系统构建了决策决策系统。然后,利用局部密度因子提出了一种基于TSIS的去噪邻域粗糙集(DNRS)模型,达到特征选择的目的,可以减弱噪声对样本数据的影响;随后,采用多元经验模态分解(MEMD)和多元核极值学习机(MKELM)进行分解和预测。最后,将提出的TSIS预测方法应用于股票价格。实验结果表明,TSIS方法对股票价格的预测具有优异的性能,可以为股票市场的量化交易提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
期刊最新文献
Insulator defect detection from aerial images in adverse weather conditions A review of the emotion recognition model of robots Knowledge guided relation enhancement for human-object interaction detection A modified dueling DQN algorithm for robot path planning incorporating priority experience replay and artificial potential fields A non-parameter oversampling approach for imbalanced data classification based on hybrid natural neighbors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1