Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction

Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction

In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Xiao, Jue, Deng, Tingting, Bi, Shuochen
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Learning
Quantitative Finance - Statistical Finance
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Xiao, Jue
Deng, Tingting
Bi, Shuochen
description In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.
doi_str_mv 10.48550/arxiv.2411.05790
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2411_05790</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2411_05790</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2411_057903</originalsourceid><addsrcrecordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjE01DMwNbc04GQIcc7PLUgsSizJLEtVcMxLzKkszixWyE9T8AkO8dVRcA8K1VFIzEtRCClKzCtOyy_KTS1S8M1PSc0pVgDyFIJL8pOzFQKKMpNTgWRqSmZySWZ-Hg8Da1piTnEqL5TmZpB3cw1x9tAF2x9fUJSZm1hUGQ9yRzzYHcaEVQAAZIA80g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction</title><source>arXiv.org</source><creator>Xiao, Jue ; Deng, Tingting ; Bi, Shuochen</creator><creatorcontrib>Xiao, Jue ; Deng, Tingting ; Bi, Shuochen</creatorcontrib><description>In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.</description><identifier>DOI: 10.48550/arxiv.2411.05790</identifier><language>eng</language><subject>Computer Science - Learning ; Quantitative Finance - Statistical Finance</subject><creationdate>2024-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2411.05790$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2411.05790$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Xiao, Jue</creatorcontrib><creatorcontrib>Deng, Tingting</creatorcontrib><creatorcontrib>Bi, Shuochen</creatorcontrib><title>Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction</title><description>In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.</description><subject>Computer Science - Learning</subject><subject>Quantitative Finance - Statistical Finance</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjE01DMwNbc04GQIcc7PLUgsSizJLEtVcMxLzKkszixWyE9T8AkO8dVRcA8K1VFIzEtRCClKzCtOyy_KTS1S8M1PSc0pVgDyFIJL8pOzFQKKMpNTgWRqSmZySWZ-Hg8Da1piTnEqL5TmZpB3cw1x9tAF2x9fUJSZm1hUGQ9yRzzYHcaEVQAAZIA80g</recordid><startdate>20241020</startdate><enddate>20241020</enddate><creator>Xiao, Jue</creator><creator>Deng, Tingting</creator><creator>Bi, Shuochen</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241020</creationdate><title>Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction</title><author>Xiao, Jue ; Deng, Tingting ; Bi, Shuochen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2411_057903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Learning</topic><topic>Quantitative Finance - Statistical Finance</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiao, Jue</creatorcontrib><creatorcontrib>Deng, Tingting</creatorcontrib><creatorcontrib>Bi, Shuochen</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiao, Jue</au><au>Deng, Tingting</au><au>Bi, Shuochen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction</atitle><date>2024-10-20</date><risdate>2024</risdate><abstract>In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.</abstract><doi>10.48550/arxiv.2411.05790</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2411.05790
ispartof
issn
language eng
recordid cdi_arxiv_primary_2411_05790
source arXiv.org
subjects Computer Science - Learning
Quantitative Finance - Statistical Finance
title Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T18%3A11%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparative%20Analysis%20of%20LSTM,%20GRU,%20and%20Transformer%20Models%20for%20Stock%20Price%20Prediction&rft.au=Xiao,%20Jue&rft.date=2024-10-20&rft_id=info:doi/10.48550/arxiv.2411.05790&rft_dat=%3Carxiv_GOX%3E2411_05790%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true