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]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/Some-simple-questions-about-neural-networks-tp12898957p14814193.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
