Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting

Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting

This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We extend several widely used optimization algorithms—incl...

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Bibliographic Details
Main Authors: Mustapha Ez-zaiym, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, Vasile Palade
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
fractional derivative
long short-term memory
financial forecasting
Online Access:https://www.mdpi.com/2227-7390/13/13/2068
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