In BlasBenito/distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

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options(scipen = 9999)
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Summary

This article lists open source implementations and interesting applications of dynamic time warping I have found in the literature, patents, and other resources. It is supposed to be a living document, so it will be updated from time to time with new findings.

Open Source Implementations

This section lists several open source implementations of DTW in R and Python, in case you want to play with DTW.

R

• dtw, by Toni Giorgino: most complete DTW suite in R, with a companion paper here. It also has a Python implementation linked below.
• dtwclust, by Alexis Sarda-Espinosa: focused on applying DTW for time series clustering. It has a very comprehensive technical vignette here.
• IncDTW, by Maximilian Leodolter: efficient implementation with a companion paper, focused on fast DTW computations for real-time applications.

Python

• dtw-python: Python implementation of dtw.
• dtaidistance: fast C implementation of DTW, with a great documentation page here.
• tslearn, by Romain Tavenard: focused on time series clustering via DTW for machine learning modeling.
• fastdtw, by Stan Salvador and Philip Chan: python implementation of FastDTW, a fast approximation to DTW described in this paper.

Other Languages

The Wikipedia page on Dynamic Time Warping lists implementations in other languages in the section Open Source Software.

Applications

Industry

Wave ring a MIDI controller to create music patented by Genki.

LAMTSS, by Toshiba, for prediction and detection of operational failures in heavy machinery.

Sift software, by HAS Motion, uses DTW to find anomalies in large biomechanics datasets.

Netflix uses Dynamic Time Warping to align soundtracks and close captions.

Academia

Classifying ball trajectories in invasion sports using dynamic time warping: A basketball case study

A Dynamic Time Warping Based Algorithm to Evaluate Kinect-Enabled Home-Based Physical Rehabilitation Exercises for Older People

Integrating Dynamic Time Warping and K-means clustering for enhanced cardiovascular fitness assessment

A Unified Framework for Quality Indexing and Classification of Seismocardiogram Signals

Using dynamic time warping for online temporal fusion in multisensor

Dynamic Time Warping as a Means of Assessing Solar Wind Time Series

BlasBenito/distantia documentation built on Feb. 21, 2025, 2:48 a.m.