Is there any difference in the architecture of a neural net for regression (time series prediction) and for classification?

Question

Is there any difference in the architecture of a neural net for regression (time series prediction) and for classification?

I did some regression testing but I get quite bad results.

I'm currently using a basic feed forward net, with one hidden layer with 2 to 4 neurons, tanh activation function and momentum.

Answer 1

It depends on a lot of factors :

1. In case of classification you can have a binary classification problem (where you want to discriminate between two classes) or multinomial classification problem. In both cases you could use different architectures for achieving the goal of the best data modeling.

2. In case of sequence regression - you could also use loads of different architectures - starting from normal feedforward network which receives one series as input and returns second as output to a lot different recurent architectures.

So the question you asked is similiar to : are tools useful for building cars different from tools useful for building bridges - it's too ambiguous and you have to specify more details.