ftsa (version 6.4)

mftsc: Multiple funtional time series clustering

Description

Clustering the multiple functional time series. The function uses the functional panel data model to cluster different time series into subgroups

Usage

mftsc(X, alpha)

Value

iteration

the number of iterations until convergence

memebership

a list of all the membership matrices at each iteration

member.final

the final membership

Arguments

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

Author

Chen Tang, Yanrong Yang and Han Lin Shang

Details

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.

See Also

MFPCA

Examples

if (FALSE) {
data(sim_ex_cluster)
cluster_result<-mftsc(X=sim_ex_cluster, alpha=0.99)
cluster_result$member.final
}