Time Series with Matrix Profile

Francisco Bischoff

16 Oct 2026

What’s New — v0.5.1 (October 2026)

tsmp is now a pure-R analytical layer (NeedsCompilation: no), delegating all high-performance computation to the matrixprofiler C++ engine. This brings orders-of-magnitude speed gains and trivial cross-platform installation.

Key highlights:

🚀 Native multithreading via n_workers — no more doSNOW/foreach clusters.
🧹 Removed dependencies: doSNOW, foreach, audio.
🔢 Standardized invalid indices — all profile index vectors now use NA_integer_ (previously -1L).
⚡ MPX is now a first-class algorithm: tsmp(..., mode = "mpx").
🔧 find_snippet() gained n_workers and a long-standing edge-case fix.

📄 Full API changes, breaking changes, and migration guide: MIGRATION.md

A broader introduction to the matrixprofiler backend 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)
MPX (single and multi-thread — native C++ via matrixprofiler)
Time Series Chains
Multivariate STOMP (mSTOMP) — native multithreading via n_workers
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 — native C++ and multi-core
Time Series Snippets — parallel n_workers support and edge-case fix
Subsetting Matrix Profiles (head(), tail(), [, etc.)
Misc:
- MASS v2.0 / v3.0 (multi-variant, multi-thread)
- MASS extensions: ADP (Approximate Distance Profile, with PAA)
- MASS extensions: WQ (Weighted Query)
- MASS extensions: QwG (Query with Gap)
- Fast moving statistics (mov_mean, mov_std, etc.) with na_strategy control

Roadmap

Migrate computation to C++ engine (matrixprofiler)
Remove doSNOW/foreach — native multithreading via n_workers
Standardize invalid-index convention to NA_integer_
MPX as first-class algorithm in tsmp()
Parallel MPdist and Time Series Snippets
CRAN-compliant pure-R package (NeedsCompilation: no)
Profile-Based Shapelet Discovery
GPU-STOMP integration
Extended tsmp() NA strategies for irregular time series

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

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.

README