| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| README.md | 2023-06-18 | 1.3 kB | |
| Release of version 0.13.0 source code.tar.gz | 2023-06-18 | 2.6 MB | |
| Release of version 0.13.0 source code.zip | 2023-06-18 | 2.8 MB | |
| Totals: 3 Items | 5.4 MB | 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.BOSStransformation 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:chunksizeallows for computing the decomposition of all the input time series using chunks (it should be a bit slower but use less memory), andn_jobsallows 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 withestimator_inpyts.classification.TimeSeriesForestandpyts.classification.TSBF(to match the changes made in scikit-learn).