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A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.

Project description

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Comprehensive implementation of Dynamic Time Warping algorithms. Supports arbitrary local (eg symmetric, asymmetric, slope-limited) and global (windowing) constraints, fast native code, several plot styles, and more.

Welcome

This package provides the most complete, freely-available (GPL) implementation of Dynamic Time Warping-type (DTW) algorithms up to date. It is a faithful Python equivalent of R’s DTW package.

The package is described in a companion paper, including detailed instructions and extensive background on things like multivariate matching, open-end variants for real-time use, interplay between recursion types and length normalization, history, etc.

Description

DTW is a family of algorithms which compute the local stretch or compression to apply to the time axes of two timeseries in order to optimally map one (query) onto the other (reference). DTW outputs the remaining cumulative distance between the two and, if desired, the mapping itself (warping function). DTW is widely used e.g. for classification and clustering tasks in econometrics, chemometrics and general timeseries mining.

The R implementation in dtw provides:

  • arbitrary windowing functions (global constraints), eg. the Sakoe-Chiba band and the Itakura parallelogram;
  • arbitrary transition types (also known as step patterns, slope constraints, local constraints, or DP-recursion rules). This includes dozens of well-known types:
  • partial matches: open-begin, open-end, substring matches
  • proper, pattern-dependent, normalization (exact average distance per step)
  • the Minimum Variance Matching (MVM) algorithm (Latecki et al.)

Multivariate timeseries can be aligned with arbitrary local distance definitions, leveraging the {proxy}dist function. DTW itself becomes a distance function with the dist semantics.

In addition to computing alignments, the package provides:

  • methods for plotting alignments and warping functions in several classic styles (see plot gallery);
  • graphical representation of step patterns;
  • functions for applying a warping function, either direct or inverse;
  • both fast native (C) and interpreted (R) cores.

Documentation

The best place to learn how to use the package (and a hopefully a decent deal of background on DTW) is the companion paper Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package, which the Journal of Statistical Software makes available for free.

To have a look at how the dtw package is used in domains ranging from bioinformatics to chemistry to data mining, have a look at the list of citing papers.

A link to prebuilt documentation is here.

Citation

If you use dtw, do cite it in any publication reporting results obtained with this software. Please follow the directions given in citation("dtw"), i.e. cite:

Toni Giorgino (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24, doi:10.18637/jss.v031.i07.

When using partial matching (unconstrained endpoints via the open.begin/open.end options) and/or normalization strategies, please also cite:

Paolo Tormene, Toni Giorgino, Silvana Quaglini, Mario Stefanelli (2008). Matching Incomplete Time Series with Dynamic Time Warping: An Algorithm and an Application to Post-Stroke Rehabilitation. Artificial Intelligence in Medicine, 45(1), 11-34. doi:10.1016/j.artmed.2008.11.007

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2019-08-22)

  • First release on PyPI.

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