I am using multiple regression to predict the future value of a time series from several other time series. Would doing this with multilayer perceptrons produce better results than multiple regression?
Edit/expansion: Assuming a multiple regression equation behaves like a single perceptron, then as far as I understand it will not be able to cope with the XOR problem, and only uses a linear hyperplane to separate the data. This suggests to me that it can only deal with very simple relationships between the variables.
A multilayer perceptron, on the other hand, is a universal function approximater, and can cope with the XOR problem. But its disadvantage could be that of over-fitting, as it has many more internal variables than a multiple regression equation.
A remark elsewhere on this forum suggested that perceptions, and thus by analogy a multiple regression equation, could be tricked in to dealing with the XOR problem. Instead of having for example the equation (leaving out all the weightings) Y=A+B+C, use the equation Y=A+B+C+AB+BC+AC+ABC. I do not know if this would work.
The advantage of multiple regression over a perception is that the former is quick to calculate using standard statistical methods. A supplementary question is, could such a things as a multi-layered multiple regression equation(s) be created and used?