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
Vol 16, No 3: June 2026

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.