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

knitr::opts_chunk$set(
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Overview

User-friendly and flexible algorithm for time series segmentation with a regression model governed by a hidden Markov process.

Hidden Markov Model Regression (HMMR) for segmentation of time series with regime changes. The model assumes that the time series is governed by a sequence of hidden discrete regimes/states, where each regime/state has Gaussian regressors as observations. The model parameters are estimated by MLE via the EM algorithm.

Installation

You can install the development version of HMMR from GitHub with:

# install.packages("devtools")
devtools::install_github("fchamroukhi/HMMR_r")

To build vignettes for examples of usage, type the command below instead:

# install.packages("devtools")
devtools::install_github("fchamroukhi/HMMR_r", 
                         build_opts = c("--no-resave-data", "--no-manual"), 
                         build_vignettes = TRUE)

Use the following command to display vignettes:

browseVignettes("HMMR")

Usage

library(HMMR)

# Application to a toy data set
data("toydataset")
x <- toydataset$x
y <- toydataset$y

K <- 5 # Number of regimes (states)
p <- 3 # Dimension of beta (order of the polynomial regressors)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter <- 1500
threshold <- 1e-6
verbose <- TRUE

hmmr <- emHMMR(X = x, Y = y, K, p, variance_type, n_tries, 
               max_iter, threshold, verbose)

hmmr$summary()

hmmr$plot()

# Application to a real data set
data("realdataset")
x <- realdataset$x
y <- realdataset$y2

K <- 5 # Number of regimes (states)
p <- 3 # Dimension of beta (order of the polynomial regressors)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter <- 1500
threshold <- 1e-6
verbose <- TRUE

hmmr <- emHMMR(X = x, Y = y, K, p, variance_type, 
               n_tries, max_iter, threshold, verbose)

hmmr$summary()

hmmr$plot()

Model selection

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.

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

data("toydataset")
x <- toydataset$x
y <- toydataset$y

plot(x, y, type = "l", xlab = "x", ylab = "Y")

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

selectedhmmr$plot(what = "smoothed")

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