Francisco Bischoff - 07 Apr 2020
Time Series with Matrix Profile
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
|Elapsed Time(s)||Data Size||Window Size||Threads||Lang|
# 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 (
- 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
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
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.
Please cite this work using our Journal of Open Source Software article
Van Benschoten et al., (2020). MPA: a novel cross-language API for time series analysis. Journal of Open Source Software, 5(49), 2179, https://doi.org/10.21105/joss.02179