Prediction using Neural Networks
The advantage of the usage of neural networks for prediction is that they are able to learn only from examples and, once training is finished, they can capture hidden and strongly nonlinear dependencies, even when there is significant noise in the training set.
The disadvantage is that NNs can learn the dependency valid in a certain period only. The prediction error also cannot generally be estimated in advance.
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