Welcome to DTAIDistance’s documentation!

Library for time series distances (e.g. Dynamic Time Warping) used in the DTAI Research Group. The library offers a pure Python implementation and a faster implementation in C. The C implementation has only Cython as a dependency. It is compatible with Numpy and Pandas and implemented to avoid unnecessary data copy operations.

Citing this work:

Source available on https://github.com/wannesm/dtaidistance.

Usage

• Installation
  • From PyPI
  • From Conda / Anaconda
  • From Github
  • From source
  • From C
• Dynamic Time Warping (DTW)
  • DTW Distance Measure Between Two Time Series
  • DTW and keep all warping paths
  • DTW between multiple Time series
  • DTW between multiple time series, limited to block
  • DTW based on shape
  • Multi-dimensionsal DTW
• Euclidean Distance (ED)
  • Different lenghts
• Clustering
  • Agglomerative clustering
  • K-Means DBA clustering
  • K-Medoids clustering
  • Active semi-supervised clustering
• Subsequence
  • DTW subsequence alignment
  • DTW subsequence search (KNN)
  • DTW Local Concurrences
• Sequences
  • Needleman-Wunsch sequence alignment
• Similarity vs Distance
  • Similarity
  • Squashing
• Changelist
  • Version 2.3
  • Version 2.2
  • Version 2.1
  • Version 2.0

Modules

• dtaidistance.dtw
• dtaidistance.dtw_visualisation
• dtaidistance.dtw_ndim
• dtaidistance.dtw_barycenter
• dtaidistance.ed
• clustering
  • dtaidistance.clustering.hierarchical
  • dtaidistance.clustering.kmeans
  • dtaidistance.clustering.medoids
• subsequence
  • dtaidistance.subsequence.dtw

Indices and tables

• Index
• Module Index
• Search Page