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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Panke Qin, Bo Ye, Ya Li, Zhongqi Cai, Zhenlun Gao, Haoran Qi, Yongjie Ding
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2025-08-01
Sarja:	Algorithms
Aiheet:	financial time series BiLSTM ARIMA whale optimization algorithm hybrid forecasting model
Linkit:	https://www.mdpi.com/1999-4893/18/8/517
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