Have you ever gotten any response from this post? I have similar questions
regarding the AMORE package.
Efferz wrote:>
> 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
>
>
> [[alternative HTML version deleted]]
>
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