In fchamroukhi/HMMR_r: Hidden Markov Model Regression ('HMMR') for Time Series Segmentation

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(HMMR)

Introduction

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

The selection can be done for the two following parameters:

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

Data

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

data("toydataset")
x <- toydataset$x
y <- toydataset$y
plot(x, y, type = "l", xlab = "x", ylab = "Y")

Model selection with BIC

selectedhmmr <- selectHMMR(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:

selectedhmmr$summary()

selectedhmmr$plot(what = "smoothed")

fchamroukhi/HMMR_r documentation built on Aug. 8, 2019, 2:38 p.m.