已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning

计算机科学 区间(图论) 数据挖掘 人工智能 残余物 特征提取 分解 计量经济学 机器学习 算法 数学 生态学 生物 组合数学
作者
Jujie Wang,Jing Liu,Weiyi Jiang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:243: 122891-122891 被引量:3
标识
DOI:10.1016/j.eswa.2023.122891
摘要

For the purpose of managing financial risk and making investment decisions, interval stock price forecasting is essential. Currently, decomposition integration frameworks are widely used in point-valued stock price forecasting studies, mainly focusing on mining internal information. However, point forecasts are difficult to adequately capture price uncertainty and may suffer from loss of volatile information. Therefore, this paper proposes an enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning. Firstly, the interval variational modal decomposition with feedback mechanism (FIVMD) is proposed to extract internal features and can decompose interval values into interval trend and residual. FIVMD not only solves the interval decomposition challenge, but also helps to improve the internal feature extraction capability. Secondly, while considering the influencing factors more comprehensively, appropriate feature selection and compression techniques can effectively achieve external feature extraction, obtain the best influencing factors, and improve the modeling capability of high-dimensional data. Finally, the final prediction results are obtained by modeling the interval trend and residuals separately through the optimization algorithm and deep learning model to improve the prediction accuracy. The results of the empirical analysis reveal that the proposed interval decomposition integrated model has the smallest of the three evaluation metrics, where the values of interval mean average percentage errors (IMAPE) are 1.8188%, 1.1244%, 1.9001%, and 2.1542% respectively. This shows that the model is significantly more accurate and stable than the other comparative models, and it is a successful model for predicting interval-valued stock prices.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
tjzbw完成签到,获得积分10
刚刚
我是老大应助自由的秋灵采纳,获得10
2秒前
情怀应助momo采纳,获得10
3秒前
大方的依霜完成签到,获得积分10
3秒前
672发布了新的文献求助10
3秒前
酷波er应助小幅上调采纳,获得10
5秒前
7秒前
7秒前
orixero应助大方的依霜采纳,获得10
9秒前
JIAO完成签到,获得积分10
9秒前
9秒前
香蕉觅云应助科研通管家采纳,获得10
9秒前
9秒前
CipherSage应助科研通管家采纳,获得10
9秒前
orixero应助科研通管家采纳,获得10
9秒前
10秒前
bxxxxx应助科研通管家采纳,获得30
10秒前
coolkid应助科研通管家采纳,获得10
10秒前
JamesPei应助科研通管家采纳,获得10
10秒前
小焦儿发布了新的文献求助10
12秒前
xiaomu发布了新的文献求助10
14秒前
Owen应助山青采纳,获得30
16秒前
17秒前
NMZN完成签到,获得积分10
17秒前
17秒前
第二支羽毛完成签到 ,获得积分10
20秒前
积极溪灵完成签到,获得积分20
21秒前
小幅上调发布了新的文献求助10
22秒前
24秒前
26秒前
迷路的依波完成签到,获得积分10
30秒前
33秒前
雨辰完成签到,获得积分10
37秒前
鲨鱼青椒完成签到,获得积分10
38秒前
暗生崎乐发布了新的文献求助10
39秒前
41秒前
领导范儿应助drchen采纳,获得10
45秒前
loong发布了新的文献求助10
47秒前
cornelia发布了新的文献求助10
47秒前
48秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989832
求助须知:如何正确求助?哪些是违规求助? 3531967
关于积分的说明 11255613
捐赠科研通 3270725
什么是DOI,文献DOI怎么找? 1805035
邀请新用户注册赠送积分活动 882181
科研通“疑难数据库(出版商)”最低求助积分说明 809208