Pyts

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- 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
:class:`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 :class:`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 :class:`pyts.classification.LearningShapelets`: to 10 (to prevent a change
of the default value in scikit-learn).

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

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

- Add the *Time Series Forest* algorithm implemented as
:class:`pyts.classification.TimeSeriesForest`.

- Add the *Time Series Bag-of-Features* algorithm implemented as
:class:`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:

+ :class:`pyts.preprocessing.KBinsDiscretizer`
+ :class:`pyts.approximation.SymbolicAggregateApproximation`
+ :class:`pyts.bag_of_words.BagOfWords`
+ :class:`pyts.classification.SAXVSM`

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

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- 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 :mod:`pyts.bag_of_words` module.

- Update the *SAXVSM* classifier with the new version of *BagOfWords*.

- Add the *BagOfPatterns* algorithm in the :mod:`pyts.transformation` module.

- Add the *ROCKET* algorithm in the :mod:`pyts.transformation` module.

- Add the *LearningShapelets* algorithm in the :mod:`pyts.classification`
module.

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

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- 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 :mod:`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 <https://github.com/johannfaouzi/pyts-repro>`_ repository,
where we compare the performance of our implementations to the literature.
* A *Contributing guide* has been added.

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- 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`

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- 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