Prediction Of Gold Prices Using Hybrid Model Arima-Lstm
Time series forecasting has been gaining attention since the COVID19 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 Decem...
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| Format: | Article |
| Language: | en |
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2025
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| Online Access: | http://eprints.uthm.edu.my/12711/1/J19606_2b6d9f1252ef2908d6f1759eddb58f60.pdf http://eprints.uthm.edu.my/12711/ https://doi.org/10.17654/0972361725031 |
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| author | Mun, Woon Kah Sufahani, Suliadi Firaus Kamil, Anton Abdulbasah Mohd Nawawi, Mohd Kamal |
| author_facet | Mun, Woon Kah Sufahani, Suliadi Firaus Kamil, Anton Abdulbasah Mohd Nawawi, Mohd Kamal |
| author_sort | Mun, Woon Kah |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Time series forecasting has been gaining attention since the COVID19 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. |
| format | Article |
| id | my.uthm.eprints-12711 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my.uthm.eprints-127112025-06-25T23:51:41Z http://eprints.uthm.edu.my/12711/ Prediction Of Gold Prices Using Hybrid Model Arima-Lstm Mun, Woon Kah Sufahani, Suliadi Firaus Kamil, Anton Abdulbasah Mohd Nawawi, Mohd Kamal HG Finance Time series forecasting has been gaining attention since the COVID19 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. 2025 Article PeerReviewed text en http://eprints.uthm.edu.my/12711/1/J19606_2b6d9f1252ef2908d6f1759eddb58f60.pdf Mun, Woon Kah and Sufahani, Suliadi Firaus and Kamil, Anton Abdulbasah and Mohd Nawawi, Mohd Kamal (2025) Prediction Of Gold Prices Using Hybrid Model Arima-Lstm. Advances and Applications in Statistics, 92 (5). 749 -766. ISSN 0972-3617 https://doi.org/10.17654/0972361725031 |
| spellingShingle | HG Finance Mun, Woon Kah Sufahani, Suliadi Firaus Kamil, Anton Abdulbasah Mohd Nawawi, Mohd Kamal Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title | Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title_full | Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title_fullStr | Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title_full_unstemmed | Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title_short | Prediction Of Gold Prices Using Hybrid Model Arima-Lstm |
| title_sort | prediction of gold prices using hybrid model arima-lstm |
| topic | HG Finance |
| url | http://eprints.uthm.edu.my/12711/1/J19606_2b6d9f1252ef2908d6f1759eddb58f60.pdf http://eprints.uthm.edu.my/12711/ https://doi.org/10.17654/0972361725031 |
| url_provider | http://eprints.uthm.edu.my/ |