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All Journal International Journal of Electrical and Computer Engineering
Phu, Lam Hung
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Computer Science & IT Electrical & Electronics Engineering

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An enhancement of stock price forecasting based on hybrid BiLSTM-Transformer model Vuong, Pham Hoang; Phu, Lam Hung; Duy, Le Nhat; Bao, Pham The; Trinh, Tan Dat
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1298-1306

Abstract

Stock price forecasting presents a challenging problem due to factors like nonlinearity, seasonality, and economic volatility in financial data. Deep learning approaches can handle nonlinearity and complexity of financial data, but they often face limitations in capturing both local and global dependencies. This study introduces a hybrid Transformer–bidirectional long short-term memory (BiLSTM) model to improve stock price forecasting. Our method combines the strength of BiLSTM with the global context understanding of the Transformer by embedding a 1D convolutional layer. The model can efficiently capture short-term and long-term dependencies in stock data. Experimental results on various datasets show that our hybrid model outperforms other well-known models.
Co-Authors Bao, Pham The Duy, Le Nhat Trinh, Tan Dat Vuong, Pham Hoang