Hybrid LSTM–Transformer Architecture with Multi-Scale Feature Fusion for High-Accuracy Gold Futures Price Forecasting

Hybrid LSTM–Transformer Architecture with Multi-Scale Feature Fusion for High-Accuracy Gold Futures Price Forecasting

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing lon...

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Bibliographic Details
Main Authors: Yali Zhao, Yingying Guo, Xuecheng Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
LSTM–transformer hybrid model
XGBoost machine learning
deep learning framework
multi-head self-attention architecture
gold futures price forecasting
Online Access:https://www.mdpi.com/2227-7390/13/10/1551
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