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README

Francisco Bischoff - 18 Aug 2022

Time Series with Matrix Profile

Packagist lifecycle CRAN version CRAN Downloads CircleCI build status

Build Dev
Windows AppVeyor build status AppVeyor build status
Coverage codecov codecov

Notice

This version is being maintained to keep up with CRAN standards. As soon as possible a new version (with possible breaking changes) with less dependencies will be released later in 2022 or beginning of 2023.

Overview

R Functions implementing UCR Matrix Profile Algorithm (http://www.cs.ucr.edu/~eamonn/MatrixProfile.html).

This package allows you to use the Matrix Profile concept as a toolkit.

This package provides:

# Basic workflow:
matrix <- tsmp(data, window_size = 30) %>%
  find_motif(n_motifs = 3) %T>%
  plot()

# SDTS still have a unique way to work:
model <- sdts_train(data, labels, windows)
result <- sdts_predict(model, data, round(mean(windows)))

Please refer to the User Manual for more details.

Please be welcome to suggest improvements.

Performance on an Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz using a random walk dataset

set.seed(2018)
data <- cumsum(sample(c(-1, 1), 40000, TRUE))

Current version benchmark

WIP in this version

Installation

# Install the released version from CRAN
install.packages("tsmp")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("matrix-profile-foundation/tsmp")

Currently available Features

Roadmap

Other projects with Matrix Profile

Matrix Profile Foundation

Our next step unifying the Matrix Profile implementation in several programming languages.

Visit: Matrix Profile Foundation

Package dependencies

Code of Conduct

Please note that the ‘tsmp’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.