Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package

By Toni Giorgino. 2009

1,265 citations recorded by the source corpus.

Dynamic time warping is a popular technique for comparing time series, providing both a distance measure that is insensitive to local compression and stretches and the warping which optimally deforms one of the two input series onto the other. A variety of algorithms and constraints have been discussed in the literature. The dtw package provides an unification of them; it allows R users to compute time series alignments mixing freely a variety of continuity constraints, restriction windows, endpoints, local distance definitions, and so on. The package also provides functions for visualizing alignments and constraints using several classic diagram types.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/51bcfef92aef96df37b25bd1ed7c096142107007

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “Next, we determine an optimal mapping between the metagenes, using dynamic time warping (DTW) as implemented in the dtw R package 62 with default parameters.”
Touch me once and i know it's you!: implicit authentication based on touch screen patterns (2012, methodology): “Thus, it is highly appropriate for the purpose of this work, for which we used the DTW implementation for R by Toni Giorgino [15].”
Similarity measures for identifying material parameters from hysteresis loops using inverse analysis (2018, methodology): “The R dtw package was used in this work [37].”
Under Consideration for Publication in Knowledge and Information Systems Improving Svm Classification on Imbalanced Time Series Data Sets with Ghost Points (neutral): “For a nice overview of elastic sequence matching algorithms, see [12].”
TSclust: An R Package for Time Series Clustering (2014, methodology): “The deﬁnition of the DTW distance is given by In TSclust , d F and d DTW are computed by using the R packages longitudinalData (Genolini 2014) and dtw (Giorgino 2009), respectively.”