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An improved wavelet–ARIMA approach for forecasting metal prices

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  • Kriechbaumer, Thomas
  • Angus, Andrew
  • Parsons, David
  • Rivas Casado, Monica

Abstract

Metal price forecasts support estimates of future profits from metal exploration and mining and inform purchasing, selling and other day-to-day activities in the metals industry. Past research has shown that cyclical behaviour is a dominant characteristic of metal prices. Wavelet analysis enables to capture this cyclicality by decomposing a time series into its frequency and time domain. This study assesses the usefulness of an improved combined wavelet-autoregressive integrated moving average (ARIMA) approach for forecasting monthly prices of aluminium, copper, lead and zinc. The performance of ARIMA models in forecasting metal prices is demonstrated to be increased substantially through a wavelet-based multiresolution analysis (MRA) prior to ARIMA model fitting. The approach demonstrated in this paper is novel because it identifies the optimal combination of the wavelet transform type, wavelet function and the number of decomposition levels used in the MRA and thereby increases the forecast accuracy significantly. The results showed that, on average, the proposed framework has the potential to increase the accuracy of one month ahead forecasts by $53/t for aluminium, $126/t for copper, $50/t for lead and $51/t for zinc, relative to classic ARIMA models. This highlights the importance of taking into account cyclicality when forecasting metal prices.

Suggested Citation

  • Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
  • Handle: RePEc:eee:jrpoli:v:39:y:2014:i:c:p:32-41
    DOI: 10.1016/j.resourpol.2013.10.005
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    Cited by:

    1. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    2. repec:eee:ecmode:v:68:y:2018:i:c:p:570-585 is not listed on IDEAS
    3. Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro & Iglesias Rodríguez, Francisco J. & Sánchez Lasheras, Fernando & de Cos Juez, F. Javier, 2015. "Investment in new tungsten mining projects," Resources Policy, Elsevier, vol. 46(P2), pages 177-190.
    4. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
    5. repec:eee:jrpoli:v:54:y:2017:i:c:p:9-24 is not listed on IDEAS
    6. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
    7. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.

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