Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network

Stock price prediction remains a complex challenge in financial markets. This study introduces a novel Long Short-Term Memory (LSTM) model optimized by Sand Cat Swarm Optimization (SCSO) for stock price prediction. The research evaluates multiple algorithms including ANN, LSTM variants, Auto-ARIMA,...

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Bibliografski detalji

Glavni autor:	Burak Gülmez
Format:	Artigo
Jezik:	Inglês
Izdano:	Elsevier 2024-12-01
Serija:	Borsa Istanbul Review
Teme:	Stock price prediction Sand Cat swarm optimization LSTM Deep learning Artificial intelligence in finance Financial forecasting
Online pristup:	http://www.sciencedirect.com/science/article/pii/S221484502400156X
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