Release of version 0.13.0

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).