Forecasting the Unknown Dynamics in NN3 Database Using a Nonlinear Autoregressive Recurrent Neural Network

In this paper, a nonlinear autoregressive (NAR) recurrent neural network is used for the prediction of the next 18 data samples of each time series in a set of 11 unknown dynamics in NN3 Database. The models are built on the reconstructed state spaces of data and no other domain knowledge is available to be used. Here, we clarify that the employed method is in part similar to a superior subclass of recurrent neural network, namely the nonlinear autoregressive model with exogenous inputs (NARX). Using the extensive available research about NARX networks, we briefly explain that our model is preferred to the both non-recursive and even other recurrent predictors, because of its intrinsic ability for learning long term dependencies in time series. As the desired values of the predicted time series are not available yet, no analysis have been performed on the presented results.