Yann Cabanes

Add stationary time series classification

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Perform stationary time series classification in a product Riemannian manifold.

## Checklist - [ ] My pull request has a clear and explanatory title. - [ ] If neccessary, my code is [vectorized](https://www.geeksforgeeks.org/vectorization-in-python/). - [ ] I have added apropriate...

Add the JAX backend

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Add the JAX backend: https://jax.readthedocs.io/en/latest/ The JAX backend can be used for automatic differentiation: https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html

List of improvements expected for tslearn version 0.6.2

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For the version 0.6.2 of tslearn, the following improvements are expected: - [ ] Fix the test error with MacOS for Python 3.9 (`Segmentation fault`): * `test_symmetric_cdist` --> The failing...

Fix problem in selection of the principal directions in Tangent PCA.

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This PR is related to PR https://github.com/geomstats/geomstats/pull/1513/ in which a solution is proposed. The current Tangent PCA algorithm does not necessarily select the axis which corresponds to the largest variance....

Add a notebook about unidimensional complex stationary time series classification

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In this notebook, we simulate complex one-dimensional stationary centered Gaussian autoregressive time series. These simulated time series will be represented in the `ProductPositiveRealsAndComplexPoincareDisks` manifold. We use the Burg algorithm to...

This pull request improves `gak` in which an overflow can occur when used on long almost constant time series. This pull request is related to the issue https://github.com/tslearn-team/tslearn/issues/510 and the...