Super learner ensemble model: A novel approach for predicting monthly copper price in future
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Resources Policy, 2023, 85, pp. 103903
- Issue Date:
- 2023-08-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0301420723006141-main.pdf | Published version | 11.41 MB |
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Companies and governments dependent on copper mining need to be able to predict copper prices in order to make important decisions. Despite the nonlinear and nonstationary nature of copper prices, their periods may vary as they fluctuate due to potential growth, cyclical fluctuations and errors. A trend-cycle refers to the combination of trend and cyclical components. Trend-cycles are characterized by different characteristics, which are crucial to making predictions. Therefore, this study focuses on developing and proposing a novel model based on an ensemble machine learning technique to predict monthly copper prices in the future by using different soft computing methods, including multi-layer perception (MLP) neural network, support vector regression (SVR), and extreme gradient boosting (XGBoost). The monthly copper price dataset from August 2001 to August 2021 was gathered for this aim based on the 14 effective parameters. These parameters were selected based on the suggestions of previous research. The main novelty of this study is the development most accurate model to predict monthly copper prices using Cubist algorithm-based super learner model as the new predictive system. The results indicated that the proposed super learner models outperformed of MLP, SVR, and XGBoost models based on the determination coefficient (R-squared), value account for (VAF), root mean square of errors (RMSE), Accuracy (Acc) and Mean Absolute Relative Error (MARE). A comprehensive comparison of different artificial intelligence models demonstrates that MLP neural network is the best model for predicting monthly copper prices. However, the standalone models involving MLP, SVR, and XGBoost, presented higher error with an RMSE in the interval of [278.3826 - 502.6946], and MARE in the interval of [0.056 - 0.1277]. Hence, Cubist-based super learner can be employed as a reliable system to predict monthly copper prices in the future. Besides, this study presents a rational mathematical model based on gene expression programming (GEP) for copper price prediction in future and for use by other researchers. Noteworthy, the final step of the study was sensitivity analysis conducting, which the results revealed that “lead price” and “euro to USD” parameters have respectively the lowest and highest impact on the monthly copper price.
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