Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting

IF 2.6 4区经济学 Q1 ECONOMICS Portuguese Economic Journal Pub Date : 2024-02-20 DOI:10.1007/s10258-024-00252-x

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Abstract

Copper is one of the main non-ferrous metals which are closely associated with important industries, such as equipment manufacturing, electrical wiring, and construction; and thus, copper price is becoming an important impact factor on the performance of related economies. This paper aims to develop a hybrid method for forecasting the copper price by combining empirical wavelet transform (EWT), particle swarm optimization (PSO), gravitational search algorithm (GSA) and long short term memory neural network (LSTM), which is denoted as EWT-PSO-GSA-LSTM in this study. The forecasting performance of the proposed hybrid method was verified by time series data of the copper closing price in the London Metal Exchange (LME). The results of this study have shown that the proposed EWT-PSO-GSA-LSTM method outperformed other forecasting methods in terms of several performance criteria, such as the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the Diebold–Mariano (DM) test. For the daily copper price time series, the EWT-PSO-GSA-LSTM method had the smallest RMSE, MAE and MAPE values (0.007, 0.013 and 1.358, respectively) compared to LSTM, EWT-LSTM, PSO-LSTM and EWT-PSO-LSTM methods. Furthermore, all the DM values of our proposed method were below -2.61 and the \(p\) values were smaller than 1%, indicating that the proposed method performed the best in forecasting the copper price at the 99% confidence level. Given the present results, it can be concluded that it is possible to improve the copper price forecasting method by combining the EWT, PSO, GSA and LSTM models.

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经验小波变换、粒子群优化、引力搜索算法和长短期记忆神经网络在铜价预测中的应用

摘要铜是主要有色金属之一，与装备制造、电线、建筑等重要行业密切相关，因此铜价正成为相关经济运行的重要影响因素。本文旨在通过将经验小波变换（EWT）、粒子群优化（PSO）、引力搜索算法（GSA）和长短期记忆神经网络（LSTM）相结合，开发一种预测铜价的混合方法，本研究将其命名为 EWT-PSO-GSA-LSTM。伦敦金属交易所（LME）铜收盘价的时间序列数据验证了所提出的混合方法的预测性能。研究结果表明，就均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和 Diebold-Mariano 检验（DM）等性能标准而言，所提出的 EWT-PSO-GSA-LSTM 方法优于其他预测方法。就每日铜价时间序列而言，与 LSTM、EWT-LSTM、PSO-LSTM 和 EWT-PSO-LSTM 方法相比，EWT-PSO-GSA-LSTM 方法的 RMSE、MAE 和 MAPE 值最小（分别为 0.007、0.013 和 1.358）。此外，我们所提出的方法的 DM 值均低于-2.61，且 \（p\）值均小于 1%，这表明所提出的方法在 99% 置信度下对铜价的预测效果最佳。鉴于上述结果，我们可以得出结论：通过结合 EWT、PSO、GSA 和 LSTM 模型，可以改进铜价预测方法。

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来源期刊

Portuguese Economic Journal ECONOMICS-

CiteScore

3.40

自引率

7.70%

发文量

期刊介绍： The Portuguese Economic Journal publishes high-quality theoretical, empirical, applied or policy-oriented research papers on any field in economics. We enforce a rigorous, fair and prompt refereeing process. The geographical reference in the name of the journal only means that the journal is an initiative of Portuguese scholars. There is no bias in favour of particular topics and issues.Officially cited as: Port Econ J