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Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm

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  • Jiahao Chen
  • Jiahui Yi
  • Kailei Liu
  • Jinhua Cheng
  • Yin Feng
  • Chuandi Fang

Abstract

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.

Suggested Citation

  • Jiahao Chen & Jiahui Yi & Kailei Liu & Jinhua Cheng & Yin Feng & Chuandi Fang, 2023. "Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0285631
    DOI: 10.1371/journal.pone.0285631
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    References listed on IDEAS

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