Employing a Novel Metaheuristic Algorithm to Optimize an LSTM Model: A Case Study of Stock Market Prediction

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


It has been long since researchers as well as investors and stakeholders who are actively pursuing financial markets are trying to analyze stock price movements and predict its trend more accurately. To minimize the forecasting risk and make the most profit, several methods have been used among which Deep Learning was at the center of attention in recent years. Deep learning techniques include analyses of historical data and recognizing patterns that can assist scientists to make a more precise prediction. This paper focuses on the application of a optimization approach called Neural Network Algorithm to optimize Long short-term Memory for the prediction of financial time series. The findings reveal that the utilization of an optimization technique such as Neural Network Algorithm to optimize Long Short-term Memory neural networks results in a notable improvement of 40%, 65%, 4%, and 85% in the MAPE, Theil U, R, and RMSE metrics, respectively. Consequently, this leads to even more accurate results and more precise predictions.
TitelAdvances in Computing and Data Sciences : 7th International Conference, ICACDS 2023, Kolkata, India, April 27–28, 2023, Revised Selected Papers
HerausgeberMayank Singh, Vipin Tyagi, P.K. Gupta, Jan Flusser, Tuncer Ören
Anzahl der Seiten12
VerlagSpringer International Publishing AG
ISBN (Print)978-3-031-37939-0
ISBN (elektronisch)978-3-031-37940-6
PublikationsstatusErschienen - 01.01.2023
VeranstaltungInternational Conference on Advances in Computing and Data Sciences - Kolkata, Indien
Dauer: 27.04.202328.04.2023
Konferenznummer: 7

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