A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships

A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships

Energy stock price prediction is a pivotal challenge in financial forecasting, characterized by high volatility and complexity influenced by geopolitical factors, regulatory shifts, and sector-specific issues. Traditional methods often struggle to account for the intricate dependencies and temporal...

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Bibliographic Details
Main Authors: Babak Amiri, Amirali Haddadi, Kosar Farajpour Mojdehi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Stock price
energy
graph neural networks
long short-term memory
dynamic time warping
graph convolutional networks
Online Access:https://ieeexplore.ieee.org/document/10858154/
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https://ieeexplore.ieee.org/document/10858154/

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