Metadata-Version: 2.1
Name: tsai
Version: 0.2.17
Summary: Practical Deep Learning for Time Series / Sequential Data library based on fastai v2/ Pytorch
Home-page: https://github.com/timeseriesAI/tsai/tree/master/
Author: Ignacio Oguiza
Author-email: oguiza@gmail.com
License: Apache Software License 2.0
Description: <div align="center">
            <img width="60%" src="./docs/images/tsai_logo.svg">
        </div>
        
        -----------------
        
        # tsai
        > State-of-the-art Deep Learning for Time Series and Sequence Modeling. `tsai` is currently under active development by timeseriesAI.
        
        
        ![CI](https://github.com/timeseriesai/tsai/workflows/CI/badge.svg) 
        [![PyPI](https://img.shields.io/pypi/v/tsai?color=blue&label=pypi%20version)](https://pypi.org/project/tsai/#description)
        [![Downloads](https://pepy.tech/badge/tsai)](https://pepy.tech/project/tsai)
        
        `tsai`is 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](https://github.com/timeseriesAI/tsai/blob/master/tutorial_nbs/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:
        ![LSST_training](https://github.com/timeseriesAI/tsai/blob/master/nbs/multimedia/LSST_PD.gif?raw=true)
        
        ## 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](https://timeseriesai.github.io/tsai/).
        
        ## 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](https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/01_Intro_to_Time_Series_Classification.ipynb)**
        
        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:
        
        ```text
        @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}
        }
        ```
        
Keywords: fastai time-series time-series-classification time-series-regression deep-learning Pytorch
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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
