https://github.com/arundo/tsaug

A Python package for time series augmentation
https://github.com/arundo/tsaug

audio data-augmentation deep-learning time-series

Last synced: 9 months ago
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A Python package for time series augmentation

• Host: GitHub
• URL: https://github.com/arundo/tsaug
• Owner: arundo
• License: apache-2.0
• Created: 2019-09-27T00:38:05.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-01-11T11:16:16.000Z (almost 3 years ago)
• Last Synced: 2025-02-19T12:55:19.318Z (10 months ago)
• Topics: audio, data-augmentation, deep-learning, time-series
• Language: Python
• Homepage: https://tsaug.readthedocs.io
• Size: 21.4 MB
• Stars: 350
• Watchers: 11
• Forks: 36
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-time-series - tsaug

README

          
# tsaug

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`tsaug` is a Python package for time series augmentation. It offers a set of

augmentation methods for time series, as well as a simple API to connect

multiple augmenters into a pipeline.

See https://tsaug.readthedocs.io complete documentation.

## Installation

Prerequisites: Python 3.5 or later.

It is recommended to install the most recent **stable** release of tsaug from PyPI.

```shell

pip install tsaug

```

Alternatively, you could install from source code. This will give you the **latest**, but unstable, version of tsaug.

```shell

git clone https://github.com/arundo/tsaug.git

cd tsaug/

git checkout develop

pip install ./

```

## Examples

A first-time user may start with two examples:

- [Augment a batch of multivariate time series](https://tsaug.readthedocs.io/en/stable/quickstart.html#augment-a-batch-of-multivariate-time-series)

- [Augment a 2-channel audio sequence](https://tsaug.readthedocs.io/en/stable/quickstart.html#augment-a-2-channel-audio-sequence)

Examples of every individual augmenter can be found [here](https://tsaug.readthedocs.io/en/stable/notebook/Examples%20of%20augmenters.html)

For full references of implemented augmentation methods, please refer to [References](https://tsaug.readthedocs.io/en/stable/references.html).

## Contributing

Pull requests are welcome. For major changes, please open an issue first to

discuss what you would like to change.

Please make sure to update tests as appropriate.

Please see [Contributing](https://tsaug.readthedocs.io/en/stable/developer.html) for more details.

## License

`tsaug` is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.