Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea
This study examines the application of Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU) along with traditional econometric models in forecasting South Korea’s GDP growth. A hybrid framework is also developed, integrating these models through a meta-learner to capitalize on their c...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-08-01
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| Series: | SAGE Open |
| Online Access: | https://doi.org/10.1177/21582440251359828 |
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| Summary: | This study examines the application of Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU) along with traditional econometric models in forecasting South Korea’s GDP growth. A hybrid framework is also developed, integrating these models through a meta-learner to capitalize on their complementary strengths. LSTM, with its ability to model nonlinear relationships and capture long-term dependencies, demonstrates accuracy improvements, especially during periods of economic volatility, such as the COVID-19 pandemic. The hybrid model further enhances forecasting performance by dynamically combining the strengths of LSTM and GRU with traditional approaches. This study provides a robust methodological contribution by uniting machine learning and econometric techniques, demonstrating their combined potential for enhancing forecasting accuracy and effectively addressing the complexities of diverse economic conditions. |
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| ISSN: | 2158-2440 |