In fchamroukhi/RHLP: Regression with Hidden Logistic Process ('RHLP') for Time Series Segmentation

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Overview

Flexible and user-friendly probabilistic segmentation of time series with smooth and/or abrupt regime changes by a mixture model-based regression approach with a hidden logistic process, fitted by the EM algorithm.

Installation

You can install the RHLP package from GitHub with:

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

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

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

Use the following command to display vignettes:

browseVignettes("RHLP")

Usage

library(RHLP)

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

K <- 5 # Number of regimes (mixture components)
p <- 3 # Dimension of beta (order of the polynomial regressors)
q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter = 1500
threshold <- 1e-6
verbose <- TRUE
verbose_IRLS <- FALSE

rhlp <- emRHLP(X = x, Y = y, K, p, q, variance_type, n_tries, 
               max_iter, threshold, verbose, verbose_IRLS)

rhlp$summary()

rhlp$plot()

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

K <- 5 # Number of regimes (mixture components)
p <- 3 # Dimension of beta (order of the polynomial regressors)
q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation)
variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1
max_iter = 1500
threshold <- 1e-6
verbose <- TRUE
verbose_IRLS <- FALSE

rhlp <- emRHLP(X = x, Y = y, K, p, q, variance_type, n_tries, 
               max_iter, threshold, verbose, verbose_IRLS)

rhlp$summary()

rhlp$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 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")

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

selectedrhlp$plot(what = "estimatedsignal")

fchamroukhi/RHLP documentation built on Sept. 19, 2019, 8:04 a.m.