Gold price prediction in covid-19 era

Hansun S., Suryadibrata A.

Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia


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 R2 score of the prediction results could reach 97.242%, which is considered high and comparable with other more complex networks’ architectures available in the literature. © Muk Publications.

COVID-19; Deep Learning; Gold price; LSTM; Prediction


Journal

International Journal of Computational Intelligence in Control

Publisher: MUK Publications and Distribution

Volume 13, Issue 2, Art No , Page 29 – 34, Page Count


Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110705492&partnerID=40&md5=c672aaa4cb7ab3f8b5b28b10d0050a93

doi:

Issn: 09748571

Type:


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