rucrdtw-package: rucrdtw: Fast time series subsequence search in R

In pboesu/rucrdtw: R Bindings for the UCR Suite

rucrdtw: Fast time series subsequence search in R

Description

Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series. The remaining cumulative distance between the series after the alignment is a useful distance metric in time series data mining applications for tasks such as classification, clustering, and anomaly detection. A broad suite of DTW algorithms is implemented in R in the dtw package (Giorgino 2009).

Calculating a DTW alignment is computationally relatively expensive, and as a consequence DTW is often a bottleneck in time series data mining applications. The UCR Suite (Rakthanmanon et al. 2012) provides a highly optimized algorithm for best-match subsequence searches that avoids unnecessary distance computations and thereby enables fast DTW and Euclidean Distance queries even in data sets containing trillions of observations.

The rucrdtw package provides R bindings for the UCR Suite. In addition to queries and data stored in text files, rucrdtw also implements methods for queries and/or data that are held in memory as R objects, as well as a method to do fast similarity searches against reference libraries of time series. The following table gives a quick overview over the different methods provided by rucrdtw:

DTW method	ED method	data format	query format	Use case
ucrdtw_ff	ucred_ff	text file	text file	data sets that are too large to keep in memory
ucrdtw_fv	ucred_fv	text file	numeric vector	data sets that are too large to keep in memory
ucrdtw_vv	ucred_vv	numeric vector	numeric vector	data sets that fit into memory
ucrdtw_mv	ucred_mv	numeric matrix	numeric vector	compare query to a set of reference sequences of equal length

Examples of the functionality in this package are provided in the rucrdtw vignette:

vignette("using_rucrdtw", package = "rucrdtw")

Author(s)

Maintainer: Philipp Boersch-Supan [e-mail] (ORCID)

Authors:

• Thanawin Rakthanmanon

• Bilson Campana

• Abdullah Mueen

• Gustavo Batista

• Eamonn Keogh

References

Rakthanmanon, Thanawin, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2012. Searching and Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 262-70. ACM. doi:\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1145/2339530.2339576")}.

Giorgino, Toni (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v031.i07")}.

UCR Suite Website: http://www.cs.ucr.edu/~eamonn/UCRsuite.html

See Also

dtw-package

pboesu/rucrdtw documentation built on Jan. 17, 2024, 8:51 p.m.