mftsc: Multiple funtional time series clustering

In ftsa: Functional Time Series Analysis

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)

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

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.

Value

iteration	the number of iterations until convergence
memebership	a list of all the membership matrices at each iteration
member.final	the final membership

Author(s)

Chen Tang, Yanrong Yang and Han Lin Shang

See Also

MFPCA

Examples

## Not run: 
data(sim_ex_cluster)
cluster_result<-mftsc(X=sim_ex_cluster, alpha=0.99)
cluster_result$member.final

## End(Not run)

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.