tsai 0.2.17

Practical Deep Learning for Time Series / Sequential Data library based on fastai v2/ Pytorch

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tsai

State-of-the-art Deep Learning for Time Series and Sequence Modeling. tsai is currently under active development by timeseriesAI.

tsaiis an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

• Self-supervised learning: If you are interested in applying self-supervised learning to time series, you may want to check our new tutorial notebook: 08_Self_Supervised_TSBERT.ipynb
• New visualization: We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task for example:

Installation

You can install the latest stable version from pip using:

pip install tsai

Or you can install the cutting edge version of this library from github by doing:

pip install -Uqq git+https://github.com/timeseriesAI/tsai.git

Once the install is complete, you should restart your runtime and then run:

from tsai.all import *

Documentation

Here's the link to the documentation.

How to get started

To get to know the tsai package, we'd suggest you start with this notebook in Google Colab: 01_Intro_to_Time_Series_Classification

It provides an overview of a time series classification problem using fastai v2.

If you want more details, you can get them in nbs 00 and 00a.

To use tsai in your own notebooks, the only thing you need to do after you have installed the package is to add this:

from tsai.all import *

Citing tsai

If you use tsai in your research please use the following BibTeX entry:

@Misc{tsai,
    author =       {Ignacio Oguiza},
    title =        {tsai - A state-of-the-art deep learning library for time series and sequential data},
    howpublished = {Github},
    year =         {2020},
    url =          {https://github.com/timeseriesAI/tsai}
}