I would like to use a Neural Network to predict financial time series. I come from an IT background and have some knowledge of Neural Networks and I have been reading about these:
I have been searching for R packages for them and I only found one for RNN, the RSNNS package which has elman and jordan implementations which are RNN.
So, are Recurrent Neural Networks useful to use with (financial) time series? Since they (quote from the wikipedia link on RNN cited before):
At each time step, the input is propagated in a standard feed-forward fashion, and then a learning rule is applied. The fixed back connections result in the context units always maintaining a copy of the previous values of the hidden units (since they propagate over the connections before the learning rule is applied). Thus the network can maintain a sort of state, allowing it to perform such tasks as sequence-prediction that are beyond the power of a standard multilayer perceptron.
aren't in practice the same as Time Delay Neural Networks? If not, which are the differences with Time Delay Neural Networks? Are both suitable to use with Time Series or which one is more suitable?