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Piecewise Regression (PWR) for Optimal Time Series Segmentation
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DESCRIPTION
NAMESPACE
PWR.Rproj
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README.md

README.md

Overview

PWR: Piecewise Regression (PWR) for time series (or structured longitudinal data) modeling and optimal segmentation by using optimized dynamic programming.

Installation

You can install the PWR package from GitHub with:

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

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

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

Use the following command to display vignettes:

browseVignettes("PWR")

Usage

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

K <- 5 # Number of segments
p <- 3 # Polynomial degree

pwr <- fitPWRFisher(X = x, Y = y, K, p)

pwr$summary()
#> --------------------
#> Fitted PWR model
#> --------------------
#> 
#> PWR model with K = 5 components:
#> 
#> Clustering table (Number of observations in each regimes):
#> 
#>   1   2   3   4   5 
#> 100 120 200 100 150 
#> 
#> Regression coefficients:
#> 
#>       Beta(k = 1) Beta(k = 2) Beta(k = 3) Beta(k = 4) Beta(k = 5)
#> 1    6.106872e-02   -5.450955   -2.776275    122.7045    4.020809
#> X^1 -7.486945e+00  158.922010   43.915969   -482.8929   13.217587
#> X^2  2.942201e+02 -651.540876  -94.269414    609.6493  -33.787416
#> X^3 -1.828308e+03  866.675017   67.247141   -249.8667   20.412380
#> 
#> Variances:
#> 
#>  Sigma2(k = 1) Sigma2(k = 2) Sigma2(k = 3) Sigma2(k = 4) Sigma2(k = 5)
#>       1.220624      1.110193      1.079366     0.9779733      1.028329

pwr$plot()

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

K <- 5 # Number of segments
p <- 3 # Polynomial degree

pwr <- fitPWRFisher(X = x, Y = y, K, p)

pwr$summary()
#> --------------------
#> Fitted PWR model
#> --------------------
#> 
#> PWR model with K = 5 components:
#> 
#> Clustering table (Number of observations in each regimes):
#> 
#>   1   2   3   4   5 
#>  15 130 178 113 126 
#> 
#> Regression coefficients:
#> 
#>     Beta(k = 1) Beta(k = 2) Beta(k = 3) Beta(k = 4) Beta(k = 5)
#> 1      2163.323   334.23747   1458.6530 -11445.9003  6418.36449
#> X^1  -13244.753  -125.04633  -1578.1793   9765.9713 -3572.38535
#> X^2  -86993.374    35.33532    753.8468  -2660.5976   699.64809
#> X^3  635558.069    49.12683   -113.1589    238.3246   -45.43516
#> 
#> Variances:
#> 
#>  Sigma2(k = 1) Sigma2(k = 2) Sigma2(k = 3) Sigma2(k = 4) Sigma2(k = 5)
#>       9326.335      50.71573      75.23989      110.6818      15.66317

pwr$plot()

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