A new version of pyts is released! The highlights of this release are:

• Add support for Python 3.10 and 3.11, and drop support for Python 3.7.

• Update the minimal versions required of the dependencies:

  • NumPy (>= 1.22.4)
  • SciPy (>= 1.8.1)
  • Scikit-Learn (>=1.2.0)
  • Joblib (>=1.1.1)
  • Numba (>=0.55.2)

• Add an example illustrating time series clustering using
  pyts.transformation.BOSS transformation with different metrics
  (by Lucas Plagwitz).

• Add automatic components-grouping in the Singular Spectrum Analysis
  for trend-seasonal decomposition with suitable example (by Lucas Plagwitz).

• Add two new parameters in pyts.decomposition.SingularSpectrumAnalysis:
  chunksize allows for computing the decomposition of all the input time
  series using chunks (it should be a bit slower but use less memory), and
  n_jobs allows for running the decomposition of each chunk in parallel.

• Set the number of initiations of K-means to compute the initial shapelets
  in pyts.classification.LearningShapelets: to 10 (to prevent a change
  of the default value in scikit-learn).

• Replace base_estimator_ attribute with estimator_ in
  pyts.classification.TimeSeriesForest and
  pyts.classification.TSBF (to match the changes made in scikit-learn).

A new version of pyts is released! The highlights of this release are:

• Add support for Python 3.9 and drop support for Python 3.6.

• Add the Time Series Forest algorithm implemented as pyts.classification.TimeSeriesForest.

• Add the Time Series Bag-of-Features algorithm implemented as pyts.classification.TSBF.

• Replace scikit-learn mixin classes with pyts mixin classes to have standardized docstrings.

• Update the examples in the Imaging time series section of the gallery of examples.

• Remove some constraints when discretizing time series (number of bins, time series with low variance) that impact the following classes:

  • pyts.preprocessing.KBinsDiscretizer
  • pyts.approximation.SymbolicAggregateApproximation
  • pyts.bag_of_words.BagOfWords
  • pyts.classification.SAXVSM

• Remove specific functions for the different variants of Dynamic Time Warping (all dtw_* functions), only the main pyts.metrics.dtw is kept.

A new version of pyts is released! The highlights of this release are:

• Add support for Python 3.8 and drop support for Python 3.5.

• Rework the BagOfWords algorithm to match the description of the algorithm
  in the original paper. The former version of BagOfWords is available
  as WordExtractor in the pyts.bag_of_words module.

• Update the SAXVSM classifier with the new version of BagOfWords.

• Add the BagOfPatterns algorithm in the pyts.transformation module.

• Add the ROCKET algorithm in the pyts.transformation module.

• Add the LearningShapelets algorithm in the pyts.classification
  module.

• Deprecated specific functions for Dynamic Time Warping (all dtw_* functions),
  only the main pyts.metrics.dtw is kept.

This new version has seen two major updates in the source code: DTW functions now support unequal-length time series and a new parameter has been added for the case where the cost matrix has already been precomputed; the Shapelet Transform algorithm has been added in the transformation module. Continuous integration is now performed on Azure Pipelines instead of Travis and Appveyor. The documentation has been revamped and is much more detailed.

This new release brings a lot of new features. The hierarchy of the code has been changed, with more modules, to make it clearer. Code of already implemented algorithms has been optimized. More algorithms have been implemented.

First release