In connorbrubaker/rulsif.ts: Time Series Change Point Detection Using Relative Unconstrained Least Squares Importance Fitting (RULSIF)

library(rulsif.ts)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

rulsif.ts

rulsif.ts implements relative unconstrained least squares importance fitting for the task of detecting change points in time series data. Below is a simple example of the use of this package.

series <- c(
    rnorm(50, mean = 0, sd = 0.3),
    rnorm(25, mean = 8, sd = 1),
    rnorm(75, mean = 3, sd = 0.6),
    rnorm(25, mean = 1, sd = 0.8),
    rnorm(100, mean = -5, sd = 1.5),
    rnorm(100, mean = -5, sd = 0.2),
    rnorm(50, mean = -2.5, sd = 0.4),
    rnorm(50, mean = 2, sd = 1.2)
)
d <- ts_detect(series, window_size = 3, step = 10, make_plot = TRUE)

Installation

You can install the the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("connorbrubaker/rulsif.ts")

References

• Song Liu, et al. "Change-point detection in time-series data by relative density-ratio estimation". In: Neural Networks 43 (2013), pp. 72-83. ISSN: 0893-6080. DOI: 10.1016/j.neunet.2013.01.012.

• Masashi, et al. "Direct importance estimation for covariance shift adaptation." In: Annals of the Institute of Statistical Mathematics 60.4 (2008), pp. 699-746. ISSN: 0020-3157. DOI: 10.1007/s10463-008-0197-x.

connorbrubaker/rulsif.ts documentation built on Dec. 19, 2021, 6:02 p.m.