"condor" wrote in message <lolrvp$t0i$1@newscl01ah.mathworks.com>...
> "Greg Heath" <heath@alumni.brown.edu> wrote in message <log3bp$1r7$1@newscl01ah.mathworks.com>...
> > "condor" wrote in message <lof02t$4k3$1@newscl01ah.mathworks.com>...
> > > Hello,
> > >
> > > let's say I have a sequence of matrices everyone is (200 x 200).
> > > I think that the new matrix in (t+1) can be forecasted using the previous five matrices (t, t1, t2, t3, t4).
> > > To test this hypotheses I would like to build a neural network.
> > >
> > > FIRST QUESTION: Should I use the Time Series Tool or the Fitting Tool?
> > >
> > > SECOND QUESTION: If using the Fitting tool how should I write the input?
> > > An array with 5 rows where every row contains a 200x200 matrix? or one row that contains a 1000x200 matrix?
> > >
> >
> > It would be helpful to know the physical and/or mathematical significance of the matrices.
> >
> > Why does it take a 200 dimensional vector to characterize whatever it is?
> >
> > Does dimensionality reduction make sense?
> >
> > Hope this helps
> >
> > Greg
>
> Hi Greg,
> I am doing an economic research about the CAPM. On rows I have all the observations of an economic factor in the past (1 day, 2 day, 3 day ... 200 days), on the columns I have another economic factor (1 day, 2 day, 3 day ... 200 days), the results are the returns of the company.
> 200 because my RAM is only 64 gb!!
>
> And now?
What is CAPM?
Your description doesn't make sense to me. Input and output examples are column vector pairs. "I"nputs are Idimensional and "o"utputs are Odimensional. So, for N pairs of examples:
[ I N ] = size(input)
[ O N ] = size(target)
Can you describe your problem w.r.t. this point of view?
Hope this helps.
Greg
