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Soft Computing

, Volume 22, Issue 16, pp 5323–5333 | Cite as

A combined neural network model for commodity price forecasting with SSA

  • Jue Wang
  • Xiang Li
Focus

Abstract

Commodity price forecasting is challenging full of volatility, uncertainty and complexity. In this paper, a novel modeling framework is proposed to predict the market price of commodity futures. Three types of commodity are selected as representatives: corn from agricultural products, gold from industrial metal and crude oil from energy. We decomposed the original series into independent components at various scales using singular spectrum analysis (SSA). A SSA-causality test is introduced to investigate the mutual influence between commodity futures prices. Additionally, using the SSA-smoothing scheme, we construct combined neural network models including back propagation, radial basis function and wavelet neural network to predict the commodity price. The experimental results illustrate that neural network models with the SSA outperform the benchmarks in terms of distinct measures.

Keywords

SSA Neural network Commodity price Forecasting 

Notes

Acknowledgements

This work was supported by Youth Innovation Promotion Association, CAS, National Center for Mathematics and Interdisciplinary Sciences (NCMIS), CAS and the National Natural Science Foundation of China (NSFC Nos. 71771208, 71271202).

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CEFS, MADISAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
  2. 2.School of Economics and ManagementUniversity of Chinese Academy of SciencesBeijingChina

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