Publicação
Forecasting natural gas prices using a hybrid deep learning model and news
| Resumo: | The transition to cleaner energy in the European Union prioritizes natural gas, yet the Russo-Ukrainian War caused unpredictable price fluctuations. Our study aimed to enhance predictive models by exploring GDELT data, analyzing pre- and post-war performance, and comparing deep learning models (RNN, LSTM, GRUNN). Incorporating crude oil and average tone data significantly improved predictions. Geopolitical factors necessitate further research to ensure energy security and economic development. Employing CRISP-DM methodology, we established a systematic approach to address these challenges. Our study contributes valuable insights to enhance predictions and adapt models to complex energy markets. |
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| Autores principais: | Filho, René Alexandre Porto da Franca Rocha |
| Assunto: | Natural gas Price prediction GDELT News sentiment Hybrid Deep learning Gás natural Previsão preço Sentimentos de notícias Híbrido |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | ISCTE |
| Idioma: | inglês |
| Origem: | Repositório ISCTE |
| Resumo: | The transition to cleaner energy in the European Union prioritizes natural gas, yet the Russo-Ukrainian War caused unpredictable price fluctuations. Our study aimed to enhance predictive models by exploring GDELT data, analyzing pre- and post-war performance, and comparing deep learning models (RNN, LSTM, GRUNN). Incorporating crude oil and average tone data significantly improved predictions. Geopolitical factors necessitate further research to ensure energy security and economic development. Employing CRISP-DM methodology, we established a systematic approach to address these challenges. Our study contributes valuable insights to enhance predictions and adapt models to complex energy markets. |
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