August 15, 2025MathematicsOpen Access

A Hybrid Model of Multi-Head Attention Enhanced BiLSTM, ARIMA, and XGBoost for Stock Price Forecasting Based on Wavelet Denoising

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Forecasting accuracy improves with the hybrid model, outperforming individual approaches in multiple metrics.
The model demonstrates enhanced performance with RMSE, MAE, MAPE, and R2 measures on stock data.
Integration of wavelet denoising, multi-head attention BiLSTM, ARIMA, and XGBoost enables complex pattern recognition.
The framework offers a promising solution for effective stock price forecasting and informed decision-making.

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Hybrid forecasting approach boosts stock price forecasting accuracy in the CSI 300 Index, suggesting valuable insights for investments.

Zhao et al. (2025) studied this question.

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