My problem is fairly complex, so here is a similar, simplified example.
Let's say you wanted to predict the Miles Per Gallon (fuel consumption) of a car, based on the following information: you have the weight of the vehicle in pounds, the number of cylinders in the engine, and a number which corresponds to a type of vehicle (0 = cube car, 1 = standard car, 2 = van, et. cetera), and the amount of rain within the past hour (in gallons).
Say you take all of this information, and compile it into a 4 Dimensional vector,
[Weight, Cylinders, Vehicle Class, Rain]. Because rain from 4 hours ago is still important, lets say we took multiple of these vectors from the past 5 hours, and feed them into a network, so we have something like this:
[Weight 5 hours ago, Cylinders 5 hours ago, Vehicle Class 5 hours ago, Rain 5 hours ago] [Weight 4 hours ago, Cylinders 4 hours ago, Vehicle Class 4 hours ago, Rain 4 hours ago] ... [Weight in past hour, Cylinders in past hour, Vehicle Class in past hour, Rain in past hour]
Being feed in, resulting in in "current rain amount"
Question: How would one format this problem for use in Artificial Neural Network? Can networks take vectors as inputs? Would it be better to allot each node to a single value (so each vector takes 4 nodes, one for each dimension, resulting in 20 nodes for the set)?
Because of the time changing, I've considered using a HMM/MM however, I am not dealing in exclusively categorical data. This isn't for a class or anything, I'm just learning out of pure interest... Thanks!