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Forecasting oil price trends using wavelets and hidden Markov models


  • de Souza e Silva, Edmundo G.
  • Legey, Luiz F.L.
  • de Souza e Silva, Edmundo A.


The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market.

Suggested Citation

  • de Souza e Silva, Edmundo G. & Legey, Luiz F.L. & de Souza e Silva, Edmundo A., 2010. "Forecasting oil price trends using wavelets and hidden Markov models," Energy Economics, Elsevier, vol. 32(6), pages 1507-1519, November.
  • Handle: RePEc:eee:eneeco:v:32:y:2010:i:6:p:1507-1519

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    References listed on IDEAS

    1. Giliola Frey & Matteo Manera & Anil Markandya & Elisa Scarpa, 2009. "Econometric Models for Oil Price Forecasting: A Critical Survey," CESifo Forum, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 10(1), pages 29-44, April.
    2. Robert K. Kaufmann, Stephane Dees, Pavlos Karadeloglou and Marcelo Sanchez, 2004. "Does OPEC Matter? An Econometric Analysis of Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 67-90.
    3. Ye, Michael & Zyren, John & Shore, Joanne, 2006. "Forecasting short-run crude oil price using high- and low-inventory variables," Energy Policy, Elsevier, vol. 34(17), pages 2736-2743, November.
    4. Ye, Michael & Zyren, John & Shore, Joanne, 2005. "A monthly crude oil spot price forecasting model using relative inventories," International Journal of Forecasting, Elsevier, vol. 21(3), pages 491-501.
    5. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    6. Antonio Merino & Alvaro Ortiz, 2005. "Explaining the so-called "price premium" in oil markets," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 29(2), pages 133-152, June.
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    Cited by:

    1. Mingming, Tang & Jinliang, Zhang, 2012. "A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices," Journal of Economics and Business, Elsevier, vol. 64(4), pages 275-286.


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