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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| _version_ | 1849735422024876032 |
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| author | Dong-Jin Pyo |
| author_facet | Dong-Jin Pyo |
| author_sort | Dong-Jin Pyo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5497642081543bd851e277be4d82300 |
| institution | DOAJ |
| issn | 2158-2440 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | SAGE Open |
| spelling | doaj-art-e5497642081543bd851e277be4d823002025-08-20T03:07:34ZengSAGE PublishingSAGE Open2158-24402025-08-011510.1177/21582440251359828Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South KoreaDong-Jin Pyo0Changwon National University, Republic of KoreaThis 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.https://doi.org/10.1177/21582440251359828 |
| spellingShingle | Dong-Jin Pyo Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea SAGE Open |
| title | Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea |
| title_full | Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea |
| title_fullStr | Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea |
| title_full_unstemmed | Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea |
| title_short | Enhancing GDP Growth Forecasting with LSTM, GRU, and Hybrid Model: Evidence from South Korea |
| title_sort | enhancing gdp growth forecasting with lstm gru and hybrid model evidence from south korea |
| url | https://doi.org/10.1177/21582440251359828 |
| work_keys_str_mv | AT dongjinpyo enhancinggdpgrowthforecastingwithlstmgruandhybridmodelevidencefromsouthkorea |