HAELT: A Hybrid Attentive Ensemble Learning Transformer Framework for High-Frequency Stock Price Forecasting

2025/06/09 by Thanh Dan Bui, Bui, Thanh Dan
Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Forecasting Techniques and Applications #I.2.6 #I.5.4 #I.6.5 #J.4 #Machine Learning (cs.LG) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2506.13981

openalex publication_date 2025/06/09 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

Abstract

High-frequency stock price prediction is challenging due to non-stationarity, noise, and volatility. To tackle these issues, we propose the Hybrid Attentive Ensemble Learning Transformer (HAELT), a deep learning framework combining a ResNet-based noise-mitigation module, temporal self-attention for dynamic focus on relevant history, and a hybrid LSTM-Transformer core that captures both local and long-range dependencies. These components are adaptively ensembled based on recent performance. Evaluated on hourly Apple Inc. (AAPL) data from Jan 2024 to May 2025, HAELT achieves the highest F1-Score on the test set, effectively identifying both upward and downward price movements. This demonstrates HAELT's potential for robust, practical financial forecasting and algorithmic trading.

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