Advances and Applications in Statistics

The Advances and Applications in Statistics is an internationally recognized journal indexed in the Emerging Sources Citation Index (ESCI). It provides a platform for original research papers and survey articles in all areas of statistics, both computational and experimental in nature.

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PREDICTION OF GOLD PRICES USING HYBRID MODEL ARIMA-LSTM

Authors

  • Woon Kah Mun
  • Suliadi Firaus Sufahani
  • Anton Abdulbasah Kamil
  • Mohd Kamal Mohd Nawawi

Keywords:

forecasting, prediction, gold price, ARIMA, LSTM

DOI:

https://doi.org/10.17654/0972361725031

Abstract

Time series forecasting has been gaining attention since the COVID-19 pandemic to predict sales, economics, and weather outcomes. In this research, an empirical study on a time series model was done to predict daily gold prices using historical everyday gold prices from 1st January 2020 to 31st December 2021 as the training and testing dataset. The performance of autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) and hybrid-model ARIMA-LSTM was compared through their mean absolute percentage error (MAPE) and root mean-squared error (RMSE) values. The results showed that the model with the smallest RMSE was the hybrid ARIMA-LSTM, but the model with the smallest MAPE was LSTM.

Received: October 6, 2024
Revised: October 31, 2024
Accepted: December 30, 2024

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Published

12-03-2025

Issue

Vol. 92 No. 5 (2025)

Section

Articles

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How to Cite

PREDICTION OF GOLD PRICES USING HYBRID MODEL ARIMA-LSTM. (2025). Advances and Applications in Statistics , 92(5), 749-766. https://doi.org/10.17654/0972361725031

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