Convert your timetable data (TT) to a matrix format using the table2array function.
>> data = table2array(TT);
You can use the 'pdist' function to calculate the pairwise distance between time series using the DTW distance metric. The pdist function can handle missing (NaN) values. The output of the pdist function is a condensed distance matrix.
>> dist = pdist(data(:, 2:end), @(x, y) dtw(x, y));
Then, 'linkage' function can be used to perform hierarchical clustering on the distance matrix.
>> Z = linkage(dist, 'ward');
To assign each time series to a cluster based on the hierarchical clustering result you can use the 'cluster' function. You can specify the number of clusters using the maxclust option.
>> idx = cluster(Z, 'maxclust', 3);
Finally, you can plot the clustering result using the 'gscatter' function.
>> gscatter(data(:,1), data(:,end), idx);
I hope this helps!
