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
inpyts.classification.LearningShapelets
: to 10 (to prevent a change
of the default value in scikit-learn). -
Replace
base_estimator_
attribute withestimator_
in
pyts.classification.TimeSeriesForest
and
pyts.classification.TSBF
(to match the changes made in scikit-learn).