Francisco Bischoff
26 mai 2021
R Functions implementing UCR Matrix Profile Algorithm (http://www.cs.ucr.edu/~eamonn/MatrixProfile.html).
This package will keep all core functions that will allow you to use the Matrix Profile concept as a toolkit.
This package provides (almost all) algorithms to build a Matrix Profile.
The package tsmp
will still be developed as “how we do data mining
with Matrix Profile”, keeping all slow stuff to be handled by this
optimized package.
This will not be covered here, as it is a tsmp
purpose:
- 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.
You can find the current tsmp
package here:
https://CRAN.R-project.org/package=tsmp
# Install the released version from CRAN
install.packages("matrixprofiler")
# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("matrix-profile-foundation/matrixprofiler")
- STAMP (single and multi-thread versions)
- STOMP (single and multi-thread versions)
- SCRIMP (single and multi-thread versions, not for AB-joins yet)
- MPX (single and multi-thread versions)
- Misc:
- MASS v2.0
- MASS v3.0
- MASS extensions: UN (Unnormalized Query)
- MASS extensions: WQ (Weighted Query)
- MASS extensions: ABS (Absolute Query and Data)
- Window functions like mov_mean() and others.
- STOMPi (On-line version)
- Multivariate STOMP (mSTOMP)
- SiMPle-Fast (Fast Similarity Matrix Profile for Music Analysis and Exploration)
- Exact Detection of Variable Length Motifs (VALMOD) (maybe will stay
on
tsmp
package) - MPdist: Matrix Profile Distance
- MASS extensions: ADP (Approximate Distance Profile, with PAA) (maybe)
- MASS extensions: QwG (Query with Gap)
- Time Series Chains
- 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)
- Annotation vectors (e.g., Stop-word MOTIF bias, Actionability bias)
- FLUSS Arc Plot and SiMPle Arc Plot
- Time Series Snippets
- Subsetting Matrix Profiles (
head()
,tail()
,[
, etc.)
- 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
Our next step unifying the Matrix Profile implementation in several programming languages.
Visit: Matrix Profile Foundation
Available at RPubs.
Please note that the matrixprofiler project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.