Forecasting Oil Production using DCA and LSTM Networks
| dc.contributor.author | Zhumekeshov A. | |
| dc.date.accessioned | 2024-12-24T06:03:17Z | |
| dc.date.available | 2024-12-24T06:03:17Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Natural resources are limited and very important in our industrial life and development. Oil is considered as the black gold and it is included in hundreds of industrial fields. Therefore, forecasting future oil production performance is an important aspect for the oil industry. In this study, we proposed improvements to the existing deep learning model in order to overcome limitations associated with the original model. For evaluation purpose, proposed and original deep learning models were applied on real case oil production data. The empirical results show that the proposed adjustments to the existing deep learning model achieves better forecasting accuracy. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1604 | |
| dc.language.iso | en | |
| dc.subject | Natural resources, oil industry, learning model, data, forecasting accuracy | |
| dc.title | Forecasting Oil Production using DCA and LSTM Networks | |
| dc.type | Other |