The motivation for this research paper is the application of two novel models\nin the prediction of crude oil index. The first model is a generic deep belief\nnetwork and the second model is an adaptive neural fuzzy inference system.\nFurthermore we have to emphasize on the second contribution in this paper\nwhich is the use of an extensive number of inputs including mixed and autoregressive\ninputs. Both proposed methodologies have been used in the past in\ndifferent problems such as face recognition, prediction of chromosome anomalies\netch, providing higher outputs than usual. For comparison purposes,\nthe forecasting statistical and empirical accuracy of models is benchmarked\nwith traditional strategies such as a naïve strategy, a moving average convergence\ndivergence model and an autoregressive moving average model. As it\nturns out, the proposed novel techniques produce higher statistical and empirical\nresults outperforming the other linear models. Concluding first time\nsuch research work brings such outstanding outputs in terms of forecasting\noil markets.
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