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README.Rmd
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README.Rmd
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---
title: "README"
author: "Francisco Bischoff"
date: "`r format(Sys.Date(), '- %d %b %Y')`"
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
library(tsmp)
```
<img src="man/figures/logo.png" align="right" style="float:right;" />
# Time Series with Matrix Profile
<!-- badges: start -->
[](https://choosealicense.com/licenses/apache-2.0/)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://cran.r-project.org/package=tsmp)
[](https://cran.r-project.org/package=tsmp)
[](https://app.circleci.com/pipelines/github/matrix-profile-foundation/tsmp)
<!-- badges: end -->
| | Build | Dev |
|--------------|-------|-----|
| Windows | [](https://ci.appveyor.com/project/franzbischoff/tsmp/branch/master) | [](https://ci.appveyor.com/project/franzbischoff/tsmp/branch/develop) |
| Coverage | [](https://app.codecov.io/gh/matrix-profile-foundation/tsmp) | [](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.