This article is devoted to a time series prediction scheme involving the nonlinear\nautoregressive algorithm and its applications. The scheme is implemented\nby means of an artificial neural network containing a hidden layer. As\na training algorithm we use scaled conjugate gradient (SCG) method and the\nBayesian regularization (BReg) method. The first method is applied to time\nseries without noise, while the second one can also be applied for noisy datasets.\nWe apply the suggested scheme for prediction of time series arising in oil\nand gas pricing using 50 and 100 past values. Results of numerical simulations\nare presented and discussed.
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