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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Autori principali:	Panke Qin, Bo Ye, Ya Li, Zhongqi Cai, Zhenlun Gao, Haoran Qi, Yongjie Ding
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-08-01
Serie:	Algorithms
Soggetti:	financial time series BiLSTM ARIMA whale optimization algorithm hybrid forecasting model
Accesso online:	https://www.mdpi.com/1999-4893/18/8/517
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