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Copper price movement prediction using recurrent neural networks and ensemble averaging

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Abstract

The motivation for this paper is to investigate the use of three promising types of recurrent neural networks (RNNs), i.e., the long short-term memory (LSTM) network, the bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU), when applied to the task of copper price prediction. This is done by deploying these RNNs with adequate data slicing and augmentation procedures to extract useful information from both domestic and international copper-related market indices for forecasting out-of-sample copper price movements. These RNN models are then empirically tested under various input window lengths, with the memory-free ANN model serving as a benchmark. The results show that the RNN models with memory units are superior to the memory-free ANN model, but the choice of a longer input window length may not produce a better prediction performance. To optimize the prediction results, the RNN models with relatively low forecasting errors are further combined using various ensemble averaging approaches (i.e., AVG or OLS, with the former being simpler) to integrate different model forecasts. Empirical findings show that the best ensemble prediction model is formed by combining just two RNNs (LSTM and BiLSTM) with shorter input window length, through the simpler AVG approach. Our results therefore suggest that in a sense, the “simpler is better” philosophy should apply for the deployment of RNN models for the task of copper price prediction.

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Funding

We would like to thank the Editor and the anonymous referees for their insightful comments and suggestions for the revisions of this paper. Any remaining errors are our responsibility. This research was supported by National Social Science Fund of China (No.19ZDA074).

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Authors and Affiliations

  1. School of Finance, Southwestern University of Finance and Economics, Chengdu, 611130, China

    Jian Ni & Yue Xu

  2. Guangdong Provencal Key Lab of Computer Integrated Manufacturing, Guangdong University of Technology, Guangzhou, 510006, China

    Zhi Li

  3. Shenzhen Meituan Technology Co., Ltd, Shenzhen, China

    Jun Zhao

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  1. Jian Ni
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  4. Jun Zhao
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Correspondence to Jun Zhao.

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Ni, J., Xu, Y., Li, Z. et al. Copper price movement prediction using recurrent neural networks and ensemble averaging. Soft Comput 26, 8145–8161 (2022). https://doi.org/10.1007/s00500-022-07201-w

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