26 mai 2021
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.
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:
# 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.
# 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
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.