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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書誌詳細

主要な著者:	Babak Amiri, Amirali Haddadi, Kosar Farajpour Mojdehi
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2025-01-01
シリーズ:	IEEE Access
主題:	Stock price energy graph neural networks long short-term memory dynamic time warping graph convolutional networks
オンライン･アクセス:	https://ieeexplore.ieee.org/document/10858154/
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