Francisco Bischoff - 03 Mar 2020
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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.
set.seed(2018) data <- cumsum(sample(c(-1, 1), 40000, TRUE))| Elapsed Time(s) | Data Size | Window Size | Threads | Lang | |
|---|---|---|---|---|---|
mpx_par | 0.59 | 40000 | 1000 | 8 | Rcpp |
mpx | 1.94 | 40000 | 1000 | 1 | Rcpp |
stomp_par | 38.90 | 40000 | 1000 | 8 | R |
stomp | 85.13 | 40000 | 1000 | 1 | R |
scrimp | 123.07 | 40000 | 1000 | 1 | R |
stamp_par | 925.45 | 40000 | 1000 | 8 | R |
stamp | 3776.86 | 40000 | 1000 | 1 | R |
# 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")- 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
- Profile-Based Shapelet Discovery
- GPU-STOMP
- 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
Our next step unifying the Matrix Profile implementation in several programming languages.
Visit: Matrix Profile Foundation
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.