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README

Francisco Bischoff - 18 Aug 2022

Time Series with Matrix Profile

	Build	Dev
Windows
Coverage

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.

# 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

• 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

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.