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DTW (Dynamic Time Warping)

Documentation Status

cicd workflow

Comprehensive dynamic time warping module for python.
Documentation is available via ReadTheDocs.

Note: Please consider to use python-dtw package which is compatible with dtw for R.

Installation

pip install dtwalign

Features

Fast computation

by Numba

Partial alignment

  • before alignment

  • after alignment

Local constraint (step pattern)

example:

Symmetric2 AsymmetricP2 TypeIVc

Global constraint (windowing)

example:

Sakoechiba Itakura User defined

Alignment path visualization

Usage

see example

Reference

  1. Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech, and Signal Processing

  • 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.

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