MATLAB: Using interpolation to replace missing values (NaN)

Question

I have cell array each containing a sequence of values as a row vector. The sequences contain some missing values represented by NaN.

I would like to re

Answer 1

If you have access to the System Identification Toolbox, you can use the MISDATA function to estimate missing values. According to the documentation:

This command linearly interpolates missing values to estimate the first model. Then, it uses this model to estimate the missing data as parameters by minimizing the output prediction errors obtained from the reconstructed data.

Basically the algorithm alternates between estimating missing data and estimating models, in a way similar to the Expectation Maximization (EM) algorithm.

The model estimated can be any of the linear models idmodel (AR/ARX/..), or if non given, uses a default-order state-space model.

Here's how to apply it to your data:

for i=1:numel(seq)
    dat = misdata( iddata(seq{i}(:)) );
    seq{i} = dat.OutputData;
end