tseriesca: Function for annual time series clustering.

In BNPTSclust: A Bayesian Nonparametric Algorithm for Time Series Clustering

Description

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

Usage

tseriesca(data, maxiter = 500, burnin = floor(0.1 * maxiter), 
          thinning = 5, scale = TRUE, level = FALSE, trend = TRUE, deg = 2, 
          c0eps = 2, c1eps = 1, c0beta = 2, c1beta = 1, c0alpha = 2, c1alpha = 1,
          priora = TRUE, pia = 0.5, q0a = 1, q1a = 1, priorb = TRUE, 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.
scale	Flag that indicates if the time series data should be scaled to the [0,1] interval with a linear transformation as proposed by Nieto-Barajas and Contreras-Cristan (2014). If TRUE, then the time series are scaled to the [0,1] interval.
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.
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.

Details

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

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.
scale	Flag that indicates if the time series data were scaled to the [0,1] interval with a linear transformation. This will be taken as an input for the plotting functions.

Author(s)

Martell-Juarez, D.A. and Nieto-Barajas, L.E.

Examples

## Do not run
#
# data(gdp)
# tseriesca.out <- tseriesca(gdp,maxiter = 4000,level=FALSE,trend=TRUE,
#                            c0eps = 0.1,c1eps = 0.1,c0beta = 0.1,
#                            c1beta = 0.1,c0alpha = 0.1,
#                            c1alpha= 0.1)
# Make sure that chain convergence is always assessed. Run the following 
# code to show the cluster and diagnostic plots:

data(gdp)
data(tseriesca.out)
attach(tseriesca.out)

clusterplots(tseriesca.out,gdp)
diagplots(tseriesca.out)

BNPTSclust documentation built on Aug. 20, 2019, 1:04 a.m.