Multiple funtional time series clustering
Clustering the multiple functional time series. The function uses the functional panel data model to cluster different time series into subgroups
mftsc(X, alpha)
X |
A list of sets of smoothed functional time series to be clustered, for each object, it is a p x q matrix, where p is the sample size and q is the number of grid points of the function |
alpha |
A value input for adjusted rand index to measure similarity of the memberships with last iteration, can be any value big than 0.9 |
As an initial step, conventional k-means clustering is performed on the dynamic FPC scores, then an iterative membership updating process is applied by fitting the MFPCA model.
iteration |
the number of iterations until convergence |
memebership |
a list of all the membership matrices at each iteration |
member.final |
the final membership |
Chen Tang, Yanrong Yang and Han Lin Shang
## Not run: data(sim_ex_cluster) cluster_result<-mftsc(X=sim_ex_cluster, alpha=0.99) cluster_result$member.final ## End(Not run)
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