I am using R 1.8.0, and am attempting to fit a Neural Network model of a time series (here called Metrics.data). It consists of one time series variable run on its lag (AR(1)). Basically, in an OLS model it would look like Metrics.data$ewindx ~ Metrics.data$ewindx.lag1 However, I am trying to run this through a neural network estimation. So far, I have been getting convergence very quickly, and do not believe it too be true. Here is the code and output. Please note that I am using all of the values for training and testing in one matrix, as I do not care about the testing results right now, I only want to capture weights. Here is the code and output > nnet(metrics.data$ewindxlag1,metrics.data$ewindx,size=2, entropy=FALSE) # weights: 7 initial value 78858370643.085342 final value 78841786515.212158 converged a 1-2-1 network with 7 weights options were - When I run the iris3 example, the convergence looks much nicer (consisting of more than one iteration). Am I missing some fundamental understanding of this example? Thanks for any input. -------------- next part -------------- An embedded message was scrubbed... From: Erik Johnson <ebjohnso at colorado.edu> Subject: [R] nnet question Date: Mon, 26 Apr 2004 11:01:12 -0600 Size: 1622 Url: https://stat.ethz.ch/pipermail/r-help/attachments/20040426/b2c41f9c/Rnnetquestion.mht