| Name | tsml-eval JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | A package for benchmarking time series machine learning tools. |
| upload_time | 2024-04-23 13:02:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.12,>=3.8 |
| license | BSD 3-Clause License Copyright (c) The Time Series Machine Learning (tsml) developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
data-science
machine-learning
time-series
time-series-classification
time-series-regression
time-series-clustering
evaluation
benchmarking
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
[](https://github.com/time-series-machine-learning/tsml-eval/actions/workflows/release.yml)
[](https://github.com/time-series-machine-learning/tsml-eval/actions/workflows/pr_pytest.yml)
[](https://github.com/time-series-machine-learning/tsml-eval/actions/workflows/periodic_tests.yml)
[](https://tsml-eval.readthedocs.io/en/stable/)
[](https://tsml-eval.readthedocs.io/en/latest/)
[](https://codecov.io/gh/time-series-machine-learning/tsml-eval)
[](https://pypi.org/project/tsml-eval/)
[](https://anaconda.org/conda-forge/tsml-eval)
[](https://www.python.org/)
[](https://github.com/psf/black)
[](https://github.com/time-series-machine-learning/tsml-eval/blob/main/LICENSE)
[](https://mybinder.org/v2/gh/time-series-machine-learning/tsml-eval/main?filepath=examples)
# tsml-eval
`tsml-eval` contains benchmarking and evaluation tools for time series machine learning
algorithms.
The current release of `tsml-eval` is v0.2.1.
## Installation
`tsml-eval` is available on PyPI and can be installed via pip:
```console
pip install tsml-eval
```
More information available on our [documentation](https://tsml-eval.readthedocs.io/en/stable/installation.html).
## Acknowledgements
This work is supported by the UK Engineering and Physical Sciences Research Council
(EPSRC) EP/W030756/2
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