tslearn is a Python package that provides machine learning tools for the analysis of time series. This package builds on scikit-learn, numpy and scipy libraries. At some point, it should be available on PyPI (as soon as it proves sufficiently helpful for the community).

Cython
numpy
scipy
scikit-learn

Run the following command for Cython code to compile:

python setup.py build_ext --inplace

Also, for the whole package to run properly, its base directory should be appended to your Python path.

• A generators module provides Random Walks generators
• A preprocessing module provides standard time series scalers
• A metrics module provides:
  • Dynamic Time Warping (DTW) (derived from cydtw code)
  • Locally-Regularized DTW
  • Global Alignment Kernel
• A domain adaptation for time series module named adaptation contains:
  • a method for (LR-)DTW-based non linear resampling that was previously released in dtw_resample repo
• A neighbors module includes nearest neighbor algorithms to be used with time series
• A clustering module includes the following time series clustering algorithms:
  • Global Alignment kernel k-means

• Add soft-DTW to the proposed metrics
• Implement Learning Shapelets from Grabocka et al. (Conv+L2, + unsupervised)
• Add local feature extractors (TransformerMixin)
• Add k-means DBA by Petitjean et al. and soft-DTW k-means by Cuturi and Blondel
• Add metric learning for time series (Garreau et al.)
• Add automatic retrieval of UCR/UEA datasets and 1M remote sensing time series
• Add LB_Keogh for nearest neighbor search
• Add Cost-Aware Early Classification of TS (Tavenard & Malinowski)?