I have two time series of meteorological measurements (i.e., X and Y). Both X and Y time series were constructed using daily measurements over a period of one year. By plotting X time series versus Y times series as a scatterplot and connecting all the points by date in ascending order, a closed loop is obtained representing the annual cycle. I have measurements at N locations and thus I have N loops (i.e., annual cycles) which I want to cluster to find those that have similar shapes.
With so many clustering methods, I am not sure which one will be more appropriate to use for this analysis (initially I was thinking to use self-organizing maps).
Thank you very much for any suggestions.
Unless you have too many time series, I suggest to start with hierarchical clustering. It's easy to interpret because of the dendrogram.
For similarity, a cyclic version of DTW may be good, assuming that there is some delay between different locations.