Weight-Training Ensemble Model for Stock Price Forecast

Jianing Zhao, Ayana Takai, E. Kita
{"title":"Weight-Training Ensemble Model for Stock Price Forecast","authors":"Jianing Zhao, Ayana Takai, E. Kita","doi":"10.1109/ICDMW58026.2022.00024","DOIUrl":null,"url":null,"abstract":"The ensemble model is applied for the stock price prediction in this study. The proposed ensemble model is based on the weighted average estimation of the values predicted by base algorithms. The base algorithms include Linear Regression, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and lightGBM. The performance of the proposed model depends on the weight parameters. The past data are collected to calculate the weigh parameters for base models of the ensemble models. The stock price prediction of Toyota Motor Corporation is considered as the numerical examples. Then LSTM, SVR and LightGBM are built to recognize the trend of the weight sequence data and to predict the most suitable combination weights for ensemble. The experimental results show that any ensemble models achieves significantly better accuracy than each component model. The proposed model also achieved the lowest error than simple average and error-based combination method. Even a tiny difference in choosing associated combining weights can play a crucial role in linear combination of models for prediction.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The ensemble model is applied for the stock price prediction in this study. The proposed ensemble model is based on the weighted average estimation of the values predicted by base algorithms. The base algorithms include Linear Regression, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and lightGBM. The performance of the proposed model depends on the weight parameters. The past data are collected to calculate the weigh parameters for base models of the ensemble models. The stock price prediction of Toyota Motor Corporation is considered as the numerical examples. Then LSTM, SVR and LightGBM are built to recognize the trend of the weight sequence data and to predict the most suitable combination weights for ensemble. The experimental results show that any ensemble models achieves significantly better accuracy than each component model. The proposed model also achieved the lowest error than simple average and error-based combination method. Even a tiny difference in choosing associated combining weights can play a crucial role in linear combination of models for prediction.
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
股票价格预测的权重训练集合模型
本文采用集合模型对股票价格进行预测。所提出的集成模型是基于对基本算法预测值的加权平均估计。基本算法包括线性回归、长短期记忆(LSTM)、支持向量回归(SVR)和lightGBM。所提模型的性能取决于权重参数。收集过去的数据,计算出集合模型基本模型的权重参数。以丰田汽车公司的股价预测为数值实例。然后构建LSTM、SVR和LightGBM来识别权值序列数据的变化趋势,预测最适合集成的组合权值。实验结果表明,任意集成模型的精度都明显优于单个组件模型。与简单平均和基于误差的组合方法相比,该模型的误差最小。在选择相关组合权值时,即使是很小的差异也会在预测模型的线性组合中起关键作用。
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
自引率
0.00%
发文量
0
期刊最新文献
Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning Backdoor Poisoning of Encrypted Traffic Classifiers Identifying Patterns of Vulnerability Incidence in Foundational Machine Learning Repositories on GitHub: An Unsupervised Graph Embedding Approach Data-driven Kernel Subspace Clustering with Local Manifold Preservation Persona-Based Conversational AI: State of the Art and Challenges
×
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