Metadata-Version: 2.1
Name: nixtlats
Version: 0.0.7
Summary: Machine learning for time series forecasting
Home-page: https://github.com/Nixtla/nixtlats/tree/master/
Author: Kin Gutiérrez, Cristian Challú, Federico Garza, Alejando Álvarez, and contributors
Author-email: fede.garza.ramirez@gmail.com
License: MIT License
Description: # `nixtlats`:Deep Learning for Time Series Forecasting
        > [nikstla] (noun, nahuatl) Period of time.
        
        
        <img src="https://raw.githubusercontent.com/Nixtla/nixtlats/master/nbs/indx_imgs/nixtla_logo.png">
        
        [![CI](https://github.com/Nixtla/nixtlats/actions/workflows/ci.yml/badge.svg?)](https://github.com/Nixtla/nixtlats/actions/workflows/ci.yml)
        [![Python](https://img.shields.io/pypi/pyversions/nixtlats)](https://pypi.org/project/nixtlats/)
        [![PyPi](https://img.shields.io/pypi/v/nixtlats?color=blue)](https://pypi.org/project/nixtlats/)
        [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/Nixtla/nixtlats/blob/master/LICENSE)
        
        State-of-the-art time series forecasting for pytorch.
        
        Nixtla is a python library for time series forecasting with deep learning. 
        It provides dataset loading utilities, evaluation functions and pytorch implementations of state of the art deep learning forecasting models.
        
        
        ## Documentation
        Here is a link to the [documentation](https://nixtla.github.io/nixtlats/).
        
        
        ## Installation
        
        ### Stable version
        
        This code is a work in progress, any contributions or issues are welcome on
        GitHub at: https://github.com/Nixtla/nixtlats.
        
        You can install the *released version* of `nixtlats` from the [Python package index](https://pypi.org) with:
        
        ```python
        pip install nixtlats
        ```
        
        (installing inside a python virtualenvironment or a conda environment is recommended).
        
        ### Development version in development mode
        
        If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:
        
        ```bash
        git clone https://github.com/Nixtla/nixtlats.git
        cd nixtlats
        pip install -e .
        ```
        
        
        ## Current available models
        
        * [Exponential Smoothing Recurrent Neural Network (ES-RNN)](https://www.sciencedirect.com/science/article/pii/S0169207019301153): A hybrid model that combines the expressivity of non linear models to capture the trends while it normalizes using a Holt-Winters inspired model for the levels and seasonals.  This model is the winner of the M4 forecasting competition.
        
        * [Neural Basis Expansion Analysis (N-BEATS)](https://arxiv.org/abs/1905.10437): A model from Element-AI (Yoshua Bengio’s lab) that has proven to achieve state of the art performance on benchmark large scale forecasting datasets like Tourism, M3, and M4. The model is fast to train an has an interpretable configuration.
        
        * [Neural Basis Expansion Analysis with Exogenous Variables (N-BEATSx)](https://arxiv.org/abs/2104.05522): The neural basis expansion with exogenous variables is an extension to the original N-BEATS that allows it to include time dependent covariates.
        
        
        ## License
        This project is licensed under the MIT License - see the [LICENSE](https://github.com/Nixtla/nixtlats/blob/master/LICENSE) file for details.
        
        ## How to contribute
        
        See [CONTRIBUTING.md](https://github.com/Nixtla/nixtlats/blob/master/CONTRIBUTING.md).
        
        ## How to cite
        
        If you use `Nixtla` in a scientific publication, we encourage you to add
        the following references to the related papers:
        
        
        ```bibtex
        @article{nixtla_arxiv,
          author  = {XXXX},
          title   = {{nixtlats: Deep Learning for Time Series Forecasting}},
          journal = {arXiv preprint arXiv:XXX.XXX},
          year    = {2021}
        }
        ```
        
        
        <!---
        
        ## Citing
        
        ```bibtex
        @article{,
            author = {},
            title = {{}},
            journal = {},
            year = {}
        }
        ```
        -->
        
Keywords: machine learning,time series,forecasting,deep learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
