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https://github.com/matrix-profile-foundation/tsmp

R Functions implementing UCR Matrix Profile Algorithm
https://github.com/matrix-profile-foundation/tsmp

algorithm matrix-profile motif-search r r-package time-series

Last synced: 2 days ago
JSON representation

R Functions implementing UCR Matrix Profile Algorithm

Host: GitHub
URL: https://github.com/matrix-profile-foundation/tsmp
Owner: matrix-profile-foundation
License: other
Created: 2018-08-05T20:37:42.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2022-08-24T05:18:09.000Z (over 2 years ago)
Last Synced: 2025-01-17T20:37:59.208Z (10 days ago)
Topics: algorithm, matrix-profile, motif-search, r, r-package, time-series
Language: R
Homepage: https://matrix-profile-foundation.github.io/tsmp
Size: 18.9 MB
Stars: 71
Watchers: 13
Forks: 18
Open Issues: 18
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        ---

title: "README"

author: "Francisco Bischoff"

date: "`r format(Sys.Date(), '- %d %b %Y')`"

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)

library(tsmp)

```



# Time Series with Matrix Profile

  

[![Packagist](https://img.shields.io/badge/License-Apache--2.0-brightgreen.svg)](https://choosealicense.com/licenses/apache-2.0/)

[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

[![CRAN version](http://www.r-pkg.org/badges/version/tsmp)](https://cran.r-project.org/package=tsmp)

[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/tsmp)](https://cran.r-project.org/package=tsmp)

[![CircleCI build status](https://circleci.com/gh/matrix-profile-foundation/tsmp.svg?style=svg)](https://app.circleci.com/pipelines/github/matrix-profile-foundation/tsmp)

  

|              | Build | Dev |

|--------------|-------|-----|

| Windows | [![AppVeyor build status](https://ci.appveyor.com/api/projects/status/byfyqncr60ten98g/branch/master?svg=true)](https://ci.appveyor.com/project/franzbischoff/tsmp/branch/master) | [![AppVeyor build status](https://ci.appveyor.com/api/projects/status/byfyqncr60ten98g/branch/develop?svg=true)](https://ci.appveyor.com/project/franzbischoff/tsmp/branch/develop) |

| Coverage | [![codecov](https://codecov.io/gh/matrix-profile-foundation/tsmp/branch/master/graph/badge.svg)](https://app.codecov.io/gh/matrix-profile-foundation/tsmp) | [![codecov](https://codecov.io/gh/matrix-profile-foundation/tsmp/branch/develop/graph/badge.svg)](https://app.codecov.io/gh/matrix-profile-foundation/tsmp) |

## 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:

* Algorithms to build a Matrix Profile: STAMP, STOMP, SCRIMP++, SIMPLE, MSTOMP and VALMOD.

* Algorithms for MOTIF search for Unidimensional and Multidimensional Matrix Profiles.

* Algorithm for Chains search for Unidimensional Matrix Profile.

* Algorithms for Semantic Segmentation (FLUSS) and Weakly Labeled data (SDTS).

* Algorithm for Salient Subsections detection allowing MDS plotting.

* Basic plotting for all outputs generated here.

* Sequencial workflow, see below.

```{r basic, examples, eval = FALSE}

# 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](https://matrixprofile.org/tsmp/reference/) 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

```{r bench_data, eval=FALSE, include=TRUE}

set.seed(2018)

data <- cumsum(sample(c(-1, 1), 40000, TRUE))

```

#### Current version benchmark

WIP in this version

```{r benchmark, eval = FALSE, cache=TRUE, include=FALSE}

w <- 200

workers <- 6

data_size <- 5000

set.seed(2018)

times <- 5L

mbm_stomp <- round(median(microbenchmark::microbenchmark(tsmp(data, window_size = w, mode = "stomp", verbose = 0), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

mbm_stomp_par <- round(median(microbenchmark::microbenchmark(tsmp(data, window_size = w, mode = "stomp", n_workers = workers, verbose = 0), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

mbm_scrimp <- round(median(microbenchmark::microbenchmark(tsmp(data, window_size = w, mode = "scrimp", verbose = 0), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

times <- 10L

mbm_mpx <- round(median(microbenchmark::microbenchmark(mpx(data, window_size = w), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

mbm_mpx_par <- round(median(microbenchmark::microbenchmark(mpx(data, window_size = w, n_workers = workers), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

times <- 3L

mbm_stamp <- round(median(microbenchmark::microbenchmark(tsmp(data, window_size = w, mode = "stamp", verbose = 0), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

mbm_stamp_par <- round(median(microbenchmark::microbenchmark(tsmp(data, window_size = w, mode = "stamp", n_workers = workers, verbose = 0), times = times, setup = (data <- cumsum(sample(c(-1, 1), data_size, TRUE))))$time) / 10^9, 2)

```

```{r bench_dataset, eval = FALSE, echo = FALSE, message=FALSE, warnings=FALSE}

bench_data <- data.frame("Elapsed Time(s)" = c(mbm_stamp, mbm_stamp_par, mbm_stomp, mbm_stomp_par, mbm_scrimp, mbm_mpx, mbm_mpx_par), "Data Size" = data_size, "Window Size" = w, Threads = c(1, workers, 1, workers, 1, 1, workers), Lang = c("R", "R", "R", "R", "R", "Rcpp", "Rcpp"), row.names = c("`stamp`", "`stamp_par`", "`stomp`", "`stomp_par`", "`scrimp`", "`mpx`", "`mpx_par`"), check.names = FALSE)

knitr::kable(bench_data[order(bench_data$`Elapsed Time(s)`), ])

```

## Installation

```{r installation, gh-installation, eval = FALSE}

# 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

* STAMP (single and multi-thread versions)

* STOMP (single and multi-thread versions)

* STOMPi (On-line version)

* SCRIMP (single-thread, not for AB-joins yet)

* Time Series Chains

* Multivariate STOMP (mSTOMP)

* Multivariate MOTIF Search (from mSTOMP)

* Salient Subsequences search for Multidimensional Space

* Scalable Dictionary learning for Time Series (SDTS) prediction

* FLUSS (Fast Low-cost Unipotent Semantic Segmentation)

* FLOSS (Fast Low-cost On-line Unipotent Semantic Segmentation)

* SiMPle-Fast (Fast Similarity Matrix Profile for Music Analysis and Exploration)

* Annotation vectors (e.g., Stop-word MOTIF bias, Actionability bias)

* FLUSS Arc Plot and SiMPle Arc Plot

* Exact Detection of Variable Length Motifs (VALMOD)

* MPdist: Matrix Profile Distance

* Time Series Snippets

* Subsetting Matrix Profiles (`head()`, `tail()`, `[`, etc.)

* Misc:

  * MASS v2.0

  * MASS v3.0

  * MASS extensions: ADP (Approximate Distance Profile, with PAA)

  * MASS extensions: WQ (Weighted Query)

  * MASS extensions: QwG (Query with Gap)

  * Fast moving average

  * Fast moving SD

## Roadmap

* Profile-Based Shapelet Discovery

* GPU-STOMP

## Other projects with Matrix Profile

* Python: https://github.com/target/matrixprofile-ts

* Python: https://github.com/ZiyaoWei/pyMatrixProfile

* Python: https://github.com/juanbeleno/owlpy

* Python: https://github.com/javidlakha/matrix-profile

* Python: https://github.com/shapelets/khiva-python

* R: https://github.com/shapelets/khiva-r

* Matlab: https://github.com/shapelets/khiva-matlab

* Java: https://github.com/shapelets/khiva-java

* Java: https://github.com/ensozos/Matrix-Profile

* Kotlin: https://github.com/shapelets/khiva-kotlin

* C++ (CUDA and OPENCL): https://github.com/shapelets/khiva

* CUDA: https://github.com/zpzim/STOMPSelfJoin

* CUDA: https://github.com/zpzim/SCAMP

## Matrix Profile Foundation

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

Visit: [Matrix Profile Foundation](https://matrixprofile.org)

## Package dependencies

```{r dependency_plot, echo = FALSE, fig.width = 10, fig.height = 5, message = FALSE, warning = FALSE, fig.path = "man/figures/"}

source("https://gist.githubusercontent.com/franzbischoff/3b83243dfdfa73e459935112f3f783e3/raw/239548243f984843b3a87d8cc82f99395a8ed26e/plot_dependencies.R")

plot_dependencies()

```

## Code of Conduct

Please note that the 'tsmp' project is released with a

[Contributor Code of Conduct](https://github.com/matrix-profile-foundation/tsmp/blob/master/.github/CODE_OF_CONDUCT.md).

By contributing to this project, you agree to abide by its terms.