README

Francisco Bischoff

26 mai 2021

Time Series with Matrix Profile

Important News!!!

The tsmp package is being modified to allow a great change in speed using the matrixprofiler package. We will make all efforts to keep it back compatible.

A slightly more explained text is available here.

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.

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/jbeleno/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

Benchmarks

New benchmarks for the new package matrixprofiler is available at RPubs.

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.