Metadata-Version: 2.1
Name: tsai
Version: 0.0.4
Summary: Practical Deep Learning for Time Series / Sequential Data library based on fastai v2/ Pytorch
Home-page: https://github.com/timeseriesAI/tsai
Author: Ignacio Oguiza
Author-email: oguiza@gmail.com
License: Apache Software License 2.0
Keywords: fastai2 time-series time-series-classification time-series-regression deep-learning Pytorch
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: sktime
Requires-Dist: pyunpack
Requires-Dist: fastai2
Requires-Dist: fastcore
Requires-Dist: psutil

# tsai
> Practical Deep Learning for Time Series / Sequential Data library based on fastai v2/ Pytorch.


`tsai`is a deep learning library built on top of fastai v2 / Pytorch focused on state-of-the-art methods for time series classification and regression.

## Install

You can install the **latest stable** version from pip:

`pip install tsa`

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

`pip install git+https://github.com/timeseriesAI/timeseriesAI.git@master`

In the latter case, you may also want to use install the bleeding egde fastai & fastcore libraries, in which case you need to do this:

`pip install git+https://github.com/fastai/fastcore.git@master`

`pip install git+https://github.com/fastai/fastai2.git@master`

## How to use

The only thing you need to do after you have installed the library is to add this to your notebook:

`from tsai.all import *`

To get familiarized with the library, I'd suggest you start with this notebook:

[01_Intro_to_Time_Series_Classification](https://github.com/timeseriesAI/timeseriesAI/blob/master/tutorial_nbs/01_Intro_to_Time_Series_Classification.ipynb)

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


