Shao-Lun Jin

Towards Robust Predictions Under Extreme Markets: A Hybrid Framework Integrating Adaptive Trend Decomposition and STOA‐Optimized Deep Learning

Financial time series prediction faces dual challenges: deep learning models are highly sensitive to hyperparameters, and their vulnerability under extreme market conditions leads to severe performance decay. To address this, this paper proposes the AT‐STOA‐Transformer‐BiLSTM hybrid framework. The framework employs the...