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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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858154/ |
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| author | Babak Amiri Amirali Haddadi Kosar Farajpour Mojdehi |
| author_facet | Babak Amiri Amirali Haddadi Kosar Farajpour Mojdehi |
| author_sort | Babak Amiri |
| collection | DOAJ |
| description | 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 patterns present in energy stock data. To address these limitations, this study introduces a hybrid model that integrates a Graph Convolutional Network (GCN) with an attention-enhanced Long Short-Term Memory (LSTM) architecture. By employing a graph structure derived from Dynamic Time Warping (DTW), the GCN captures inter-stock relationships, while the attention mechanism within the LSTM component refines the modelling of temporal dynamics, allowing the model to focus on the most relevant historical information. Experimental evaluations across multiple energy stocks show that this combined LSTMGC model significantly outperforms conventional approaches- including Linear Regression, GRU, MLP, and standalone LSTMs- when assessed using Mean Squared Error (MSE) and R-squared (R2) metrics. By jointly leveraging spatial and temporal dependencies, as well as the selective attention mechanism, the proposed framework enhances predictive accuracy and reliability, offering valuable insights for investors and policymakers navigating the evolving energy market. |
| format | Article |
| id | doaj-art-abd5ca3e057f42e9a8c7aff31def6b4c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-abd5ca3e057f42e9a8c7aff31def6b4c2025-02-12T00:02:49ZengIEEEIEEE Access2169-35362025-01-0113248152483210.1109/ACCESS.2025.353688910858154A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock RelationshipsBabak Amiri0https://orcid.org/0000-0001-9469-5648Amirali Haddadi1Kosar Farajpour Mojdehi2https://orcid.org/0009-0002-5048-0455School of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranEnergy 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 patterns present in energy stock data. To address these limitations, this study introduces a hybrid model that integrates a Graph Convolutional Network (GCN) with an attention-enhanced Long Short-Term Memory (LSTM) architecture. By employing a graph structure derived from Dynamic Time Warping (DTW), the GCN captures inter-stock relationships, while the attention mechanism within the LSTM component refines the modelling of temporal dynamics, allowing the model to focus on the most relevant historical information. Experimental evaluations across multiple energy stocks show that this combined LSTMGC model significantly outperforms conventional approaches- including Linear Regression, GRU, MLP, and standalone LSTMs- when assessed using Mean Squared Error (MSE) and R-squared (R2) metrics. By jointly leveraging spatial and temporal dependencies, as well as the selective attention mechanism, the proposed framework enhances predictive accuracy and reliability, offering valuable insights for investors and policymakers navigating the evolving energy market.https://ieeexplore.ieee.org/document/10858154/Stock priceenergygraph neural networkslong short-term memorydynamic time warpinggraph convolutional networks |
| spellingShingle | Babak Amiri Amirali Haddadi Kosar Farajpour Mojdehi A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships IEEE Access Stock price energy graph neural networks long short-term memory dynamic time warping graph convolutional networks |
| title | A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships |
| title_full | A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships |
| title_fullStr | A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships |
| title_full_unstemmed | A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships |
| title_short | A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships |
| title_sort | novel hybrid gcn lstm algorithm for energy stock price prediction leveraging temporal dynamics and inter stock relationships |
| topic | Stock price energy graph neural networks long short-term memory dynamic time warping graph convolutional networks |
| url | https://ieeexplore.ieee.org/document/10858154/ |
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