Hybrid BiLSTM-ARIMA Architecture with Whale-Driven Optimization for Financial Time Series Forecasting

Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning model...

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Detalles Bibliográficos

Principais autores:	Panke Qin, Bo Ye, Ya Li, Zhongqi Cai, Zhenlun Gao, Haoran Qi, Yongjie Ding
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2025-08-01
Series:	Algorithms
Assuntos:	financial time series BiLSTM ARIMA whale optimization algorithm hybrid forecasting model
Acceso en liña:	https://www.mdpi.com/1999-4893/18/8/517
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