Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.
Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0285631 |
| _version_ | 1827771145655418880 |
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| author | Jiahao Chen Jiahui Yi Kailei Liu Jinhua Cheng Yin Feng Chuandi Fang |
| author_facet | Jiahao Chen Jiahui Yi Kailei Liu Jinhua Cheng Yin Feng Chuandi Fang |
| author_sort | Jiahao Chen |
| collection | DOAJ |
| description | Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes. |
| first_indexed | 2024-03-11T12:50:28Z |
| format | Article |
| id | doaj.art-38bff3a03d4641ec889bdb5193768de1 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-11T12:50:28Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-38bff3a03d4641ec889bdb5193768de12023-11-04T05:32:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011810e028563110.1371/journal.pone.0285631Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.Jiahao ChenJiahui YiKailei LiuJinhua ChengYin FengChuandi FangCopper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes.https://doi.org/10.1371/journal.pone.0285631 |
| spellingShingle | Jiahao Chen Jiahui Yi Kailei Liu Jinhua Cheng Yin Feng Chuandi Fang Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. PLoS ONE |
| title | Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. |
| title_full | Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. |
| title_fullStr | Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. |
| title_full_unstemmed | Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. |
| title_short | Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm. |
| title_sort | copper price prediction using lstm recurrent neural network integrated simulated annealing algorithm |
| url | https://doi.org/10.1371/journal.pone.0285631 |
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