Editing Dynamic time warping (section)

==Open-source software==

* The [https://github.com/MonashTS/tempo tempo] C++ library with Python bindings implements Early Abandoned and Pruned DTW as well as Early Abandoned and Pruned ADTW and DTW lower bounds LB_Keogh, LB_Enhanced and LB_Webb.
* The [https://github.com/ChangWeiTan/UltraFastWWS UltraFastMPSearch] Java library implements the UltraFastWWSearch algorithm<ref>{{cite book |last1=Tan |first1=Chang Wei |last2=Herrmann |first2=Matthieu |last3=Webb |first3=Geoffrey I. |chapter=Ultra fast warping window optimization for Dynamic Time Warping |title=2021 IEEE International Conference on Data Mining (ICDM) |date=2021 |pages=589–598 |doi=10.1109/ICDM51629.2021.00070 |isbn=978-1-6654-2398-4 |s2cid=246291550 |chapter-url=https://changweitan.com/research/UltraFastWWSearch.pdf}}</ref> for fast warping window tuning.
* The [https://github.com/lemire/lbimproved lbimproved] C++ library implements Fast Nearest-Neighbor Retrieval algorithms under the GNU General Public License (GPL). It also provides a C++ implementation of dynamic time warping, as well as various lower bounds.
* The [https://github.com/rmaestre/FastDTW FastDTW] library is a Java implementation of DTW and a FastDTW implementation that provides optimal or near-optimal alignments with an ''O''(''N'') time and memory complexity, in contrast to the ''O''(''N''<sup>2</sup>) requirement for the standard DTW algorithm. FastDTW uses a multilevel approach that recursively projects a solution from a coarser resolution and refines the projected solution.
* [https://mvnrepository.com/artifact/com.github.davidmoten/fastdtw FastDTW fork] (Java) published to Maven Central.
* [https://github.com/cesarsotovalero/time-series-classification time-series-classification] (Java) a package for time series classification using DTW in Weka.
* The [https://dynamictimewarping.github.io/ DTW suite] provides Python ([https://pypi.org/project/dtw-python/ dtw-python]) and R packages ([https://cran.r-project.org/package=dtw dtw]) with a comprehensive coverage of the DTW algorithm family members, including a variety of recursion rules (also called step patterns), constraints, and substring matching.
* The [[mlpy]] Python library implements DTW.
* The [https://pypi.python.org/pypi/pydtw pydtw] Python library implements the Manhattan and Euclidean flavoured DTW measures including the LB_Keogh lower bounds.
* The [https://gravitino.github.io/cudadtw/ cudadtw] C++/CUDA library implements subsequence alignment of Euclidean-flavoured DTW and ''z''-normalized Euclidean distance similar to the popular UCR-Suite on CUDA-enabled accelerators.
* The [http://java-ml.sourceforge.net/ JavaML] machine learning library implements [https://sourceforge.net/p/java-ml/java-ml-code/ci/9f6726deab4e55b7617478bc51e29c20308bffb9/tree/net/sf/javaml/distance/dtw/FastDTW.java DTW].
* The [https://github.com/doblak/ndtw ndtw] C# library implements DTW with various options.
* [https://github.com/kirel/sketch-a-char Sketch-a-Char] uses Greedy DTW (implemented in JavaScript) as part of LaTeX symbol classifier program.
* The [https://github.com/hfink/matchbox MatchBox] implements DTW to match mel-frequency cepstral coefficients of audio signals.
* [https://github.com/fpetitjean/DBA Sequence averaging]: a GPL Java implementation of DBA.<ref name="DBA"/>
* The [https://github.com/nickgillian/grt/wiki Gesture Recognition Toolkit|GRT] C++ real-time gesture-recognition toolkit implements DTW.
* The [http://biointelligence.hu/pyhubs/ PyHubs] software package implements DTW and nearest-neighbour classifiers, as well as their extensions (hubness-aware classifiers).
* The [https://github.com/talcs/simpledtw simpledtw] Python library implements the classic ''O''(''NM'') Dynamic Programming algorithm and bases on Numpy. It supports values of any dimension, as well as using custom norm functions for the distances. It is licensed under the MIT license.
* The [https://tslearn.readthedocs.io/en/latest/# tslearn] Python library implements DTW in the time-series context.
*The [https://github.com/garrettwrong/cuTWED cuTWED] CUDA Python library implements a state of the art improved [[Time Warp Edit Distance]] using only linear memory with phenomenal speedups.
* [https://github.com/baggepinnen/DynamicAxisWarping.jl DynamicAxisWarping.jl] Is a Julia implementation of DTW and related algorithms such as FastDTW, SoftDTW, GeneralDTW and DTW barycenters.
* The [https://github.com/kaen2891/Multi_DTW/ Multi_DTW] implements DTW to match two 1-D arrays or 2-D speech files (2-D array).
* The [https://pypi.org/project/dtwParallel/ dtwParallel] (Python) package incorporates the main functionalities available in current DTW libraries and novel functionalities such as parallelization, computation of similarity (kernel-based) values, and consideration of data with different types of features (categorical, real-valued, etc.).<ref>{{cite journal |last1=Escudero-Arnanz |first1=Óscar |last2=Marques |first2=Antonio G |last3=Soguero-Ruiz |first3=Cristina |last4=Mora-Jiménez |first4=Inmaculada |last5=Robles |first5=Gregorio |date=2023 |title=dtwParallel: A Python package to efficiently compute dynamic time warping between time series |url=https://www.sciencedirect.com/science/article/pii/S2352711023000602 |journal=SoftwareX |volume=22 |issue=101364 |doi=10.1016/J.SOFTX.2023.101364 |bibcode=2023SoftX..2201364E |access-date=2024-12-06|hdl=10115/24752 |hdl-access=free }}</ref>