how can I interpolate NaN values and correlate a matrix

Hi anyone can help me? I have a matrix A(9x8)which corresponds to a time series records that have NaN missing values. I need to do both, first interpolate those NaN values and second cross-correlate every row with all the rest in order to get correlation values that I will use to cluster rows with similar time series behaviour according to the coefficient of correlation. The matrix is something like this:

A= ...
[48.4  34.77  47.77  NaN  42.77  44.85  46.75  47.16
45.27  37.6  48.18  NaN  46  44.38  48.48  49.19
46.35  40.81  45.51  NaN  43.29  43.38  48.29  NaN
45.38  42.25  42.86  NaN  39.62  46.05  NaN  54.28
43.84  36.63  48.13  NaN  39.62  45.37  55.55  48.49
43.36  36.82  47.88  NaN  36.58  46.91  47.05  49.56
44.54  38.38  50.35  NaN  41.98  45.32  NaN  53.45
45.5  37.68  44.2  NaN  42.88  NaN  50.27  50.02
55.50  38.62  43.48  NaN  42.08  46.35  51.53  53.43]

Thank you so much for your help, JC

Answer 1

[Oleg Komarov]

[rr,cc] = size(A);

% Values to interpolate

xi      = 1:cc;

% Index the non NaNs

idx     = ~isnan(A);

% Loop for each row and linearly interpoalte and extrapolate

for r = 1:rr
   A(r,:) = interp1(xi(idx(r,:)),A(r,idx(r,:)),xi,'linear','extrap');
end

corr(A')

[Azzi Abdelmalek]

he needs statsitcs toolbox for "corr"

[Oleg Komarov]

Yes, forgot that.

@Juan: in addition, you can simply use 'pairwise' option of corr() to avoid NaNs.