The non-linear nature of historical oil and gas spot prices makes prediction very difficult. An evaluation of historical time series spot prices data with Fourier power spectrum analysis and autocorrelation function shows the likelihood of chaotic behavior. Characterization and identification of the data with the Lyapunov exponent suggest the existence of chaos. A chaos theory analysis is therefore used for the space phase reconstruction of the strange attractors in the oil and gas markets. The optimal embedding dimension, time delay and predictability are obtained with a spatial minimization of the root mean square error. The embedding dimension and time delay are then used as inputs in a fuzzy neural network model.

The time series spot price data is embedded and divided into training and testing sets. A fuzzy neural network model is constructed using the training set and checked with the testing set. A good match is obtained between the predicted and historical time series data. The paper concludes that the chaotic behavior of the historical oil and gas spot prices prevents the long-term forecast of future spot prices and limits the short-term forecast to the embedding prediction horizon.

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