tsml-py
A repository for in-development time series machine learning algorithms and other odd bits by Matthew Middlehurst.
Please see tsml_eval and
aeon for more developed and stable packages. This package
is more of a sandbox for testing out new ideas and algorithms. It may contain some
algorithms and implementations that are not available in the other toolkits.
The current release of tsml is v0.10.0.
Installation
tsml is available on PyPI and can be installed via pip:
pip install tsml
Acknowledgements
This work is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) EP/W030756/1
Metadata
Release files for tsml 0.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tsml-0.10.0.tar.gz | 294.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tsml-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 616.7 kB
Release files / tsml-0.10.0.tar.gz
| Download URL | tsml-0.10.0.tar.gz |
|---|---|
| Size | 294.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / tsml-0.10.0-py3-none-any.whl
| Download URL | tsml-0.10.0-py3-none-any.whl |
|---|---|
| Size | 321.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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