Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy

Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy

Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms f...

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Bibliographic Details
Main Authors: Mingfu Zhu, Yaxing Liu, Panke Qin, Yongjie Ding, Zhongqi Cai, Zhenlun Gao, Bo Ye, Haoran Qi, Shenjie Cheng, Zeliang Zeng
Format: Article
Language:English
Published: PeerJ Inc. 2024-12-01
Series:PeerJ Computer Science
Subjects:
LSTM networks
Arbitrage spread forecasting
Hyperparameter setting
Cuckoo algorithm
Zebra algorithm
Online Access:https://peerj.com/articles/cs-2552.pdf
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