
Show HN: tsaug – a time series data augmentation library in Python - roycoding
https://github.com/arundo/tsaug
======
roycoding
This is my team's second time series related library that we've recently open
sourced, the other being
[https://github.com/arundo/adtk](https://github.com/arundo/adtk), our anomaly
detection toolkit for time series.

We created this library because we were training a lot of deep learning models
on time series data, but needed more examples of the specific types of
behaviour we were interested in. This data augmentation library is inspired by
image data augmentation libraries, but taking the considerations of time
series into account. As with image data, you need to carefully consider
whether the specific "augmentation" will preserve the aspects of the data that
you are interested in.

We've released tsaug under an Apache license. We'd love to have people try it
out, make contributions, and ask any questions.

tsaug is pip installable and the documentation and examples are linked in the
readme on Github.

Credit primarily goes to Tailai Wen, who led this effort.

------
tailaiw
Here is a blog article I wrote to introduce the package
[https://www.arundo.com/arundo_tech_blog/tsaug-an-open-
source...](https://www.arundo.com/arundo_tech_blog/tsaug-an-open-source-
python-package-for-time-series-augmentation)

