Ok, I have these huge data sets I want to analyze, and most of the column values relate to one or more values in other columns. Here is a simple example, in reality there are many more columns:
t c1 c2 c3 c4
0 21.0 2.0 54.2 0.1
15 22.2 2.2 51.5 0.2
30 24.4 2.9 52.3 0.4
45 19.1 3.2 58.7 0.7
60 18.6 3.4 61.3 0.6
75 ??.? ?.? ??.? ?.?
90 ??.? ?.? ??.? ?.?
To simplify, I have logged values with equal time intervals for a long time, and now I want to predict the values for c1, c2, c3 and c4 at t=75 and t=90.
If I make arrays out of these column values, eg:
float[] c1Array = { 21.0, 22.2, 24.4, 19.1, 18.6 };
float[] c2Array = { 2.0, 2.2, 2.9, 3.2, 3.4};
float[] c3Array = { 54.2,........61.3}
and so on...
eg.
c1Array[2] would inter-correspond with c2Array[2], c3Array[2] etc...
How would I utilize Accord.NET machine learning, and what are my options? I've read a bit upon using DecisionTree and ID3Learning - and it looks like that can solve my problem:
A simple example with just x and y values (I need an example with x, y, z,....)
But then I came over this, which is Hidden Markov Models in Accord.NET, and it seems like that might be a better fit.
I am a programmer, not a mathematician, and I find most examples tend to operate with single data sets that would have 1 or two "columns", I need to work on multiple.
I could work with something like the x and y values example I linked above, but with using more columns.
But if there are better functions in my case, I would love to see some examples.