Forecasting the IBOVESPA Using NARX Networks and Random Walk Model
نویسندگان
چکیده
This section presents, an important class of multivariate non-linear models for discrete time: NARX (Nonlinear AutoRegressive with eXogenous inputs) feedforward NN [2]. The use of NARX Networks for forecasting has gained popularity due to its capacity to represent nonlinear systems with good precision and due to the fact that any data which can, in some way, supply information to the series of time in study can be incorporated into the model. In order to automatize the selection of the input data and the choice of the best architecture of the network, the heuristic of pruning Optimal Brain Surgeon [1] was applied to automatize the selection of the optimal structure of Network NARX and the recursive algorithm of Gauss-Newton Exponential Forgetting for the training of the network.
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