Hansun, Seng and Suryadibrata, Alethea (2021) Gold Price Prediction in COVID-19 Era. International Journal of Computational Intelligence in Control, 13 (2). pp. 29-33. ISSN 0974-8571
|
Text
Gold Price Prediction in COVID-19 Era.pdf Download (1MB) | Preview |
Abstract
As one of the most frequently traded commodities in the world, gold has been hugely impacted by the COVID-19 crisis. In this study, we try to apply a famous Deep Learning method for time series analysis, namely the Long Short-Term Memory (LSTM) networks, for future gold price prediction. However, rather than using a complex network architecture, we propose simple three layers LSTM networks that were trained on 4,219 training records and tested on 1,055 test records. We found that the Root Mean Square Error (RMSE) value for the prediction results is 39.94162, while the Mean Absolute Percentage Error(MAPE) value is 17.66144. Moreover, the
| Item Type: | Article |
|---|---|
| Keywords: | COVID-19, Deep Learning, Gold price, LSTM, Prediction |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 003 Systems (Computer Modeling and Simulation) |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 03 Nov 2023 07:56 |
| Last Modified: | 03 Nov 2023 07:56 |
| URI: | https://kc.umn.ac.id/id/eprint/27024 |
Actions (login required)
 |
View Item |
