| title | tags | authors | date | bibliography | |||||||||||
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rucrdtw: Fast time series subsequence search in R |
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2016-11-04 |
vignettes/rucrdtw.bib |
Dynamic Time Warping (DTW) methods provide algorithms to optimally map a given time series onto all or part of another time series [@berndt1994using]. 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.
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 [@rakthanmanon2012searching] 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.
A broad suite of DTW algorithms is implemented in R in the dtw package [@giorgino2009computing]. The rucrdtw R package provides complementary functionality for fast similarity searches by providing R bindings for the UCR Suite via Rcpp [@Rcpp]. 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.
