Comparison of hyperparameter tuning optimization methods for LSTM and stock price prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63599758" target="_blank" >RIV/70883521:28140/25:63599758 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-84353-2_17" target="_blank" >http://dx.doi.org/10.1007/978-3-031-84353-2_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-84353-2_17" target="_blank" >10.1007/978-3-031-84353-2_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of hyperparameter tuning optimization methods for LSTM and stock price prediction

Popis výsledku v původním jazyce

This paper investigates using Long Short-Term Memory (LSTM) networks for predicting stock prices, focusing on major stocks like AAPL, MSFT, TSLA, META, and GOOG from 2016 to 2021. We employ several technical analysis indicators, such as moving averages, the relative strength index, and others, as inputs to our LSTM model. The study involves data preprocessing, optimization, and tuning of ten different hyperparameters to enhance the performance of LSTM model. A comparative analysis of optimization techniques, including standard random search, Bayesian, Nevergrad optimization, covariance matrix adaptation optimization, and other bio-inspired algorithms, shows variations in performance across most datasets. Performance is measured using typical statistical metrics like mean squared error, R2 score, and others, with results showing varying prediction accuracies among different stocks. The study highlights the critical influence of data quality on LSTM performance and suggests further research into optimal hyperparameter tuning for enhancing AI-driven financial analytics.

Název v anglickém jazyce

Comparison of hyperparameter tuning optimization methods for LSTM and stock price prediction

Popis výsledku anglicky

This paper investigates using Long Short-Term Memory (LSTM) networks for predicting stock prices, focusing on major stocks like AAPL, MSFT, TSLA, META, and GOOG from 2016 to 2021. We employ several technical analysis indicators, such as moving averages, the relative strength index, and others, as inputs to our LSTM model. The study involves data preprocessing, optimization, and tuning of ten different hyperparameters to enhance the performance of LSTM model. A comparative analysis of optimization techniques, including standard random search, Bayesian, Nevergrad optimization, covariance matrix adaptation optimization, and other bio-inspired algorithms, shows variations in performance across most datasets. Performance is measured using typical statistical metrics like mean squared error, R2 score, and others, with results showing varying prediction accuracies among different stocks. The study highlights the critical influence of data quality on LSTM performance and suggests further research into optimal hyperparameter tuning for enhancing AI-driven financial analytics.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Artificial Intelligence

ISBN

978-3-031-84352-5

ISSN

2945-9133

e-ISSN

1611-3349

Počet stran výsledku

13

Strana od-do

195-208

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopane

Datum konání akce

16. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001535042200017