Fast Change Point Detection via Sequential Gradient Descent • fastcpd

Overview

The fastcpd (fast change point detection) is a fast implmentation of change point detection methods in R. The fastcpd package is designed to find change points in a fast manner. It is easy to install and extensible to all kinds of change point problems with a user specified cost function apart from the built-in cost functions.

To learn more behind the algorithms:

Installation

# Install from CRAN
install.packages("fastcpd")

Development version

# Development version from r-universe with CRAN version as a fallback
install.packages(
  "fastcpd",
  repos = c("https://doccstat.r-universe.dev", "https://cloud.r-project.org")
)

## install.packages("pak")
pak::pak("doccstat/fastcpd")

## install.packages("devtools")
devtools::install_github("doccstat/fastcpd")

With mamba or conda (available soon)

# conda-forge is a fork from CRAN and may not be up-to-date

# Use mamba
mamba install r-fastcpd
# Use conda
conda install -c conda-forge r-fastcpd

FAQ

Package dependencies

fastcpd depends on the following packages:

Rcpp, for C++ source code compilation.
RcppArmadillo, for fast linear algebra.
fastglm, for fast generalized linear models.
glmnet, for penalized regression.
ggplot2, for data visualization.

If you’re compiling from source, you can run the following command to see the complete set of system packages needed on your machine.

pak::pkg_sysreqs("doccstat/fastcpd")
#> ── Install scripts ───────────────────────────────────────────── Ubuntu 20.04
#> apt-get -y update
#> apt-get -y install libcurl4-openssl-dev libssl-dev zlib1g-dev make
#>
#> ── Packages and their system dependencies ───────────────────────────────────
#> curl       – libcurl4-openssl-dev, libssl-dev
#> data.table – zlib1g-dev
#> fs         – make
#> openssl    – libssl-dev

Should I install suggested packages?

The suggested packages are not required for the main functionality of the package. They are only required for the vignettes. If you want to learn more about the package comparison and other vignettes, you could either check out vignettes on CRAN or pkgdown generated documentation.

I countered problems related to gfortran on Mac OSX or Linux!

The package should be able to install on Mac and any Linux distribution without any problems if all the dependencies are installed. However, if you encountered problems related to gfortran, it might be because RcppArmadillo is not installed previously. Try Mac OSX stackoverflow solution or Linux stackover solution if you have trouble installing RcppArmadillo.

Cheatsheet

R Shiny App

Available soon: rshiny.fastcpd.xingchi.li

Usage

set.seed(1)
n <- 1000
x <- rep(0, n + 3)
for (i in 1:600) {
  x[i + 3] <- 0.6 * x[i + 2] - 0.2 * x[i + 1] + 0.1 * x[i] + rnorm(1, 0, 3)
}
for (i in 601:1000) {
  x[i + 3] <- 0.3 * x[i + 2] + 0.4 * x[i + 1] + 0.2 * x[i] + rnorm(1, 0, 3)
}
result <- fastcpd::fastcpd.ar(x[3 + seq_len(n)], 3, r.progress = FALSE)
summary(result)
#> 
#> Call:
#> fastcpd::fastcpd.ar(data = x[3 + seq_len(n)], order = 3, r.progress = FALSE)
#> 
#> Change points:
#> 614 
#> 
#> Cost values:
#> 2743.759 2028.588 
#> 
#> Parameters:
#>     segment 1 segment 2
#> 1  0.57120256 0.2371809
#> 2 -0.20985108 0.4031244
#> 3  0.08221978 0.2290323
plot(result)

Tip

It is hard to demonstrate all the features of fastcpd in a single example due to the flexibility of the package. For more examples, please refer to the function reference.

Note

r.progress = FALSE is used to suppress the progress bar. Users are expected to see the progress bar when running the code by default.

Examples

Main function

fastcpd

Wrapper functions

Time series

AR(p): fastcpd_ar
ARIMA(p, d, q): fastcpd_arima
ARMA(p, q): fastcpd_arma
GARCH(p, q): fastcpd_garch
VAR(p): fastcpd_var
General time series: fastcpd_ts

Unlabeled data

Mean change: fastcpd_mean
Variance change: fastcpd_variance
Mean and/or variance change: fastcpd_meanvariance

Regression data

Logistic regression: fastcpd_binomial
Penalized linear regression: fastcpd_lasso
Linear regression: fastcpd_lm
Poisson regression: fastcpd_poisson

Utility functions

Variance estimation

Variance estimation in ARMA models: variance_arma
Variance estimation in linear models: variance_lm
Variance estimation in mean change models: variance_mean
Variance estimation in median change models: variance_median

Class methods

Plot: plot
Print: print
Show: show
Summary: summary

Data

Bitcoin Market Price (USD): bitcoin
Occupancy Detection Data Set: occupancy
Transcription Profiling of 57 Human Bladder Carcinoma Samples: transcriptome
UK Seatbelts Data: uk_seatbelts
Well-log Dataset from Numerical Bayesian Methods Applied to Signal Processing: well_log

Main class

fastcpd

Make contributions

We welcome contributions from everyone. Please follow the instructions below to make contributions.

Fork the repo.
Create a new branch from main branch.
Make changes and commit them.
1. Please follow the Google’s R style guide for naming variables and functions.
2. If you are adding a new family of models with new cost functions with corresponding gradient and Hessian, please add them to src/fastcpd_class_cost.cc with proper example and tests in vignettes/gallery.Rmd and tests/testthat/test-gallery.R.
3. Add the family name to src/fastcpd_constants.h.
4. [Recommended] Add a new wrapper function in R/fastcpd_wrappers.R for the new family of models and move the examples to the new wrapper function as roxygen examples.
5. Add the new wrapper function to the corresponding section in _pkgdown.yml.
Push the changes to your fork.
Create a pull request.
Make sure the pull request does not create new warnings or errors in devtools::check().

fastcpd