Francisco Bischoff - 03 Mar 2020

Time Series with Matrix Profile

	Build	Dev
Linux x86_64
OSX
Windows
Coverage

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.

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
`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

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)
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