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 scipy.io.arff.loadarff

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