Change Log

Version 0.10.0

  • Adapt DTW functions to compare time series with different lengths (by Hicham Janati)
  • Add a precomputed_cost parameter in DTW variants that are compatible with a precomputed cost matrix, that is classical DTW and DTW with global constraint regions like Sakoe-Chiba band and Itakura parallelogram (by Hicham Janati)
  • Add a new algorithm called ShapeletTransform in the pyts.transformation module.
  • Add a new dependency, the joblib Python package, since it has been vendored from scikit-learn and it is used in ShapeletTransform.
  • [DOC] Revamp documentation in most sections:
    • User guide is much more detailed
    • A Scikit-learn compatibility page has been added to highlight the compatibility of pyts estimators with scikit-learn tools like model selection and pipelines.
    • A Reproducibility page has been added to highlight the work done in the pyts-repro repository, where we compare the performance of our implementations to the literature.
    • A Contributing guide has been added.

Version 0.9.0

  • Add datasets module with dataset loading utilities
  • Add multivariate module with utilities for multivariate time series
  • Revamp the tests using pytest.mark.parametrize
  • Add an Examples section in most of the public functions and classes
  • Require version 1.3.0 of scipy: this is required to load ARFF files with relational attributes using

Version 0.8.0

  • No more Python 2 support
  • New package required: numba
  • Updated required versions of packages
  • Modification of the API:
    • quantization module merged in approximation and removed
    • bow module renamed bag_of_words
    • Fewer acronyms used for the names of the classes: if an algorithm has a name with three words or fewer, the whole name is used.
    • More preprocessing tools in preprocessing module
    • New module metrics with metrics specific to time series
  • Improved tests using pytest tools
  • Reworked documentation
  • Updated continuous integration scripts
  • More optimized code using numba