In fchamroukhi/MRHLP: Multiple Regression Model with a Hidden Logistic Process ('MRHLP') for Joint Segmentation of Multivariate Time Series

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(MRHLP)

Introduction

In this package, it is possible to select models 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 MRHLP model for the following time series $Y$:

data("toydataset")
x <- toydataset$x
y <- toydataset[, c("y1", "y2", "y3")]
matplot(x, y, type = "l", xlab = "x", ylab = "Y", lty = 1)

Model selection with BIC

selectedmrhlp <- selectMRHLP(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:

selectedmrhlp$summary()

selectedmrhlp$plot(what = "estimatedsignal")

fchamroukhi/MRHLP documentation built on Sept. 23, 2019, 4:17 a.m.