Hi,
I have some "simple" questions and annotations about neural networks:
1) Which R-package (or which software) would you use to train and validate a
multilayer (2 hidden layers) feed forward neural network. I think "AMORE" is
the only one that can do this task in R.
2) When using neural networks for time series prediction (macroeconomic &
financial time series), how would you precede to avoid overfitting? Split
the sample in two subsamples, train the NN in the first subsample and then
test it on the validation set? What is a good split ratio 1/2, 2/3, 3/4? Are
there procedureces which endogenize this step.
3) How to select the parameters like the global.learnging.rate, the
momentum.global, the activation functions of the hidden layer neurons, the
training method, the n.shows and show.step numbers, the probability
vector,... when setting up a neural net with "AMORE" or
the initial weights, decay,... when setting up a neural net with "NNET"?
Are these all "econometrican choice variables" and must be exogenously
specified. My own experience shows that the results are far from robust and
highly sensitive to an alternative parameter choice. Even when using the
same parameter setup re-training and re-validation delivers different
results (unless you use the set.seed command). How to get results that are
replicable? I think there is a great danger of getting spurious results when
snooping the parameter space. Or are there any reasons why to use a decay of
0.1 instead 0.11 or a momentum of 0.4 instead of 0.5, or ...
Is it a good choice to use if possible the default values? Therefore I am
very skeptical if those new and highly sophisticated non-linear methods
(neural networks, svm, etc.) perform really better in time series prediction
than the classical linear methods. Besides the problem which variables to
use as predictors, how to choose the calibration window (rolling expanding,
rolling fixed), one faces the additional choice of model parameters.
How do you think about it? Any ideas? Any experiences?
Best
Martin
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