Model-selection-MHMMR

In samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

library(knitr)
knitr::opts_chunk$set(
    fig.align = "center",
    fig.height = 5.5,
    fig.width = 6,
    warning = FALSE,
    collapse = TRUE,
    dev.args = list(pointsize = 10),
    out.width = "90%",
    par = TRUE
)
knit_hooks$set(par = function(before, options, envir)
  { if (before && options$fig.show != "none") 
       par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5)
})

library(samurais)

Introduction

In this package, it is possible to select models based on information criteria such as BIC, AIC and ICL.

The selection is done on two parameters which are:

• $K$: The number of regimes;
• $p$: The order of the polyniomial regression.

Data

Let's select a MHMMR model for the following multivariate time series $Y$:

data("multivtoydataset")
x <- multivtoydataset$x
y <- multivtoydataset[, c("y1", "y2", "y3")]
matplot(x, y, type = "l", xlab = "x", ylab = "Y")

Model selection with BIC

selectedmhmmr <- selectMHMMR(X = x, Y = y, Kmin = 2, Kmax = 6, pmin = 0, pmax = 3)

The selected model has $K = 5$ regimes and the order of the polynomial regression is $p = 0$. According to the way $Y$ has been generated, these parameters are what we expected.

Let's summarize the selected model:

selectedmhmmr$summary()

selectedmhmmr$plot(what = "smoothed")

samurais documentation built on July 28, 2019, 5:02 p.m.