BNPTSclust (version 1.1)

tseriescm: Function for monthly time series clustering.

Description

Function that performs the time series clustering algorithm described in Nieto-Barajas and Contreras-Cristan (2014) for monthly time series data.

Usage

tseriescm(data, maxiter = 1000, burnin = floor(0.1 * maxiter), 
          thinning = 5, level = FALSE, trend = TRUE, seasonality = TRUE,
          deg = 2,c0eps = 2, c1eps = 1, c0beta = 2, c1beta = 1, 
          c0alpha = 2,c1alpha = 1, priora = FALSE, pia = 0.5, q0a = 1,
          q1a = 1, priorb = FALSE, q0b = 1, q1b = 1, a = 0.25, b = 0,
          indlpml = FALSE)

Arguments

data

Data frame with the time series information.

maxiter

Maximum number of iterations for Gibbs sampling.

burnin

Burn-in period of the Markov Chain generated by Gibbs sampling.

thinning

Number that indicates how many Gibbs sampling simulations should be skipped to form the Markov Chain.

level

Flag that indicates if the level of the time series will be considered for clustering. If TRUE, then it is taken into account.

trend

Flag that indicates if the polinomial trend of the model will be considered for clustering. If TRUE, then it is taken into account.

seasonality

Flag that indicates if the seasonal components of the model will be considered for clustering. If TRUE, then they are taken into account.

deg

Degree of the polinomial trend of the model.

c0eps

Shape parameter of the hyper-prior distribution on sig2eps.

c1eps

Rate parameter of the hyper-prior distribution on sig2eps.

c0beta

Shape parameter of the hyper-prior distribution on sig2beta.

c1beta

Rate parameter of the hyper-prior distribution on sig2beta.

c0alpha

Shape parameter of the hyper-prior distribution on sig2alpha.

c1alpha

Rate parameter of the hyper-prior distribution on sig2alpha.

priora

Flag that indicates if a prior on parameter "a" is to be assigned. If TRUE, a prior on "a" is assigned.

pia

Mixing proportion of the prior distribution on parameter "a".

q0a

Shape parameter of the continuous part of the prior distribution on parameter "a".

q1a

Shape parameter of the continuous part of the prior distribution on parameter "a".

priorb

Flag that indicates if a prior on parameter "b" is to be assigned. If TRUE, a prior on "b" is assigned.

q0b

Shape parameter of the prior distribution on parameter "b".

q1b

Shape parameter of the prior distribution on parameter "b".

a

Initial/fixed value of parameter "a".

b

Initial/fixed value of parameter "b".

indlpml

Flag that indicates if the LPML is to be calculated. If TRUE, LPML is calculated.

Value

mstar

Number of groups of the chosen cluster configuration.

gnstar

Array that contains the group number to which each time series belongs.

HM

Heterogeneity Measure of the chosen cluster configuration.

arrho

Acceptance rate of the parameter "rho".

ara

Acceptance rate of the parameter "a".

arb

Acceptance rate of the parameter "b".

sig2epssample

Matrix that in its columns contains the sample of each sig2eps_i's posterior distribution after Gibbs sampling.

sig2alphasample

Matrix that in its columns contains the sample of each sig2alpha_i's posterior distribution after Gibbs sampling.

sig2betasample

Matrix that in its columns contains the sample of each sig2beta_i's posterior distribution after Gibbs sampling.

sig2thesample

Vector that contains the sample of sig2the's posterior distribution after Gibbs sampling.

rhosample

Vector that contains the sample of rho's posterior distribution after Gibbs sampling.

asample

Vector that contains the sample of a's posterior distribution after Gibbs sampling.

bsample

Vector that contains the sample of b's posterior distribution after Gibbs sampling.

msample

Vector that contains the sample of the number of groups at each Gibbs sampling iteration.

lpml

If indlpml = TRUE, lpml contains the value of the LPML of the chosen model.

Details

It is assumed that the time series data is organized into a data frame with the time periods included as its row names.

Examples

# NOT RUN {
## Do not run
#
# data(stocks)
# tseriescm.out <- tseriescm(stocks,maxiter=4000,level=FALSE,trend=TRUE,
#                            seasonality=TRUE,a=0,b=1)
#
# The console output of the above example is:
# 
# Number of groups of the chosen cluster configuration:  9 
# Time series in group 1 : 1 2 4 5 7 10 12 13 19 21 22 25 29 30 31 33 34 
# 40 41 42 43 44 46 47 48 49 52 57 58 
# Time series in group 2 : 3 6 8 9 11 14 15 17 18 26 27 28 32 35 36 37 38 
# 45 50 51 53 56 
# Time series in group 3 : 16 
# Time series in group 4 : 20 
# Time series in group 5 : 23 
# Time series in group 6 : 24 
# Time series in group 7 : 39 
# Time series in group 8 : 54 
# Time series in group 9 : 55 
# HM Measure:  199.2226 
#
# Make sure that chain convergence is always assessed. Run the following 
# code to show the cluster and diagnostic plots:

data(stocks)
data(tseriescm.out)
attach(tseriescm.out)

clusterplots(tseriescm.out,stocks)
diagplots(tseriescm.out)
# }