Theoretically, the advantage of NNR models over traditional forecasting methods is because, as is often the case, the model best adapted to a particular problem cannot be identified. It is then better to resort to a method that is a generalisation of many models, than to rely on an a priori model (Dunis and Huang, 2002).

However, NNR models have been criticised and their widespread success has been hindered because of their "black-box" nature, excessive training times, danger of overfitting, and the large number of "parameters" required for training. As a result, deciding on the appropriate network involves much trial and error.

For a full discussion on neural networks, please refer to Haykin (1999), Kaastra and Boyd (1996), Kingdon (1997), or Zhang et al. (1998). Notwithstanding, we provide below a brief description of NNR models and procedures.

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