Predicting Gold Price Movement Using Long Short-Term Memory Model

Authors

  • Azaria Beryl Nagata Universitas Dian Nuswantoro
  • Moch Sjamsul Hidajat Universitas Dian Nuswantoro
  • Dibyo Adi Wibowo Universitas Dian Nuswantoro
  • Widyatmoko Widyatmoko Universitas Dian Nuswantoro
  • Noorayisahbe Bt Mohd Yaacob University Kebangsaan Malaysia

DOI:

https://doi.org/10.33633/jais.v9i1.10305

Abstract

Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.

References

K. A. Manjula and P. Karthikeyan, “Gold price prediction using ensemble based machine learning techniques,” Proc. Int. Conf. Trends Electron. Informatics, ICOEI 2019, vol. 2019-April, no. April 2019, pp. 1360–1364, 2019, doi: 10.1109/icoei.2019.8862557.

Q. Ji, D. Zhang, and Y. Zhao, “Searching for safe-haven assets during the COVID-19 pandemic,” no. January, 2020.

Y. Melita Pranoto, R. Alexandro Harianto, and Iswanto, “Pemanfatan Arima Untuk Prediksi Harga Emas Dalam Sistem Rekomendasi Trading Gold Option,” J. Media Inform. Budidarma, vol. 4, no. 4, pp. 863–871, 2020, doi: 10.30865/mib.v4i4.2246.

J. Manoj and K. K. Suresh, “Forecast model for price of gold: Multiple linear regression with principal component analysis,” Thail. Stat., vol. 17, no. 1, pp. 125–132, 2019.

I. Amalou, N. Mouhni, and A. Abdali, “Multivariate time series prediction by RNN architectures for energy consumption forecasting,” Energy Reports, vol. 8, no. May, pp. 1084–1091, 2022, doi: 10.1016/j.egyr.2022.07.139.

D. Shah, H. Isah, and F. Zulkernine, “Stock market analysis: A review and taxonomy of prediction techniques,” Int. J. Financ. Stud., vol. 7, no. 2, 2019, doi: 10.3390/ijfs7020026.

H. N. Zamani, “ANALISIS KINERJA SAHAM BERDASARKAN TEKNIK ANALISIS TEKNIKAL DAN FUNDAMENTAL,” Universitas Islam Negri Maulana Malik Ibrahim, 2019. [Online]. Available: http://www.scopus.com/inward/record.url?eid=2-s2.0-84865607390&partnerID=tZOtx3y1%0Ahttp://books.google.com/books?hl=en&lr=&id=2LIMMD9FVXkC&oi=fnd&pg=PR5&dq=Principles+of+Digital+Image+Processing+fundamental+techniques&ots=HjrHeuS_

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. Shahab, “Deep learning for stock market prediction,” Entropy, vol. 22, no. 8, 2020, doi: 10.3390/E22080840.

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Published

2024-04-02