README.md

In lpishchagina/fpop2D: fpop2D

fpop2D Vignette

Liudmila Pishchagina

February 11, 2021

Quick Start

fpop2D is an R package written in Rcpp/C++ and developed to change-point detection in bivariate time-series.

The package implements the Functional Pruning Optimal Partitioning algorithm with two types of pruning: "intersection of sets" and "difference of intersection and union of sets".

We present a basic use of the main functions of the fpop2D package.

We install the package from Github:

#devtools::install_github("lpishchagina/fpop2D")
library(fpop2D)

The function data_gen2D

The data_gen2D function simulates a bivariate time series with the arguments:

n is a time series length.

changepoints is a changepoint vector that gives the last index of each segment.

The last element of changepoints always equals to the length of time series Data.

means1 is a vector of means for the first univariate time series.

The length of this vector is equal to the length of changepoints.

means2 is a vector of means for the second univariate time series.

The length of this vector is equal to the length of changepoints.

noise is a variance of the time series(by default it is equal to 1).

n <- 100
Data <- data_gen2D(n, changepoints = c(20, 40, 60, 80, 100), means1 = c(0, 1, 0, 1, 0), means2 = c(1, 2, 3, 4, 5), noise = 1)

The function FPOP2D

The FPOP2D function returns the result of the segmentation using the parameters:

data1 is the first univariate time series.

data2 is the second univariate time series.

penalty is a value of penalty (a non-negative real number).

The penalty here equals to a classic 2*(noise^2)*log(n).

type is a parameter defining the algorithm of segmentation.

The type must be either 1 or 2.

We use Functional Pruning Optimal Partitioning algorithm with the pruning "intersection of sets" when type = 1.

We use Functional Pruning Optimal Partitioning algorithm with the pruning "difference of intersection and union of sets" when type = 2.

We choose a gaussian cost.

resFPOP1 <- FPOP2D(Data[1,], Data[2,], penalty = 2 * log(n) , type = 1)

resFPOP2 <- FPOP2D(Data[1,], Data[2,], penalty = 2 * log(n) , type = 2)

## resFPOP1$changepoints
## 
## resFPOP1$means1
## 
## resFPOP1$means2
##
## resFPOP1$globalCost
##

## resFPOP2$changepoints
## 
## resFPOP2$means1
## 
## resFPOP2$means2
##
## resFPOP2$globalCost
##

changepoints is a vector of the inferred changepoints.

means1 is a vector of the inferred means for the first univariate time series.

means2 is a vector of the inferred means for the second univariate time series.

globalCost is the overall gaussian cost of the segmented data.

The function plotFPOP2D

The function plotFPOP2D plots a graph for each univariate time series of the bivariate series.

The graph shows the univariate time series, the estimated values of means and the inferred segments.

The function plotFPOP2D uses the parameters:

data is the bivariate time series.

changepoints is a vector of the inferred changepoints.

means1 is a vector of the inferred means for the first univariate time series.

means2 is a vector of the inferred means for the second univariate time series.

plotFPOP2D(data = Data, changepoints = c(20, 40, 60, 80, 100), means1 = c(0, 1, 0, 1, 0), means2 = c(1, 2, 3, 4, 5))

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lpishchagina/fpop2D documentation built on March 14, 2021, 11:05 p.m.