Metadata-Version: 2.1
Name: neuralforecast
Version: 0.0.2
Summary: Deep Learning for Time Series Forecasting
Home-page: https://github.com/Nixtla/neuralforecast/tree/main/
Author: Kin Gutiérrez, Cristian Challú, Federico Garza, Max Mergenthaler, and contributors
Author-email: fede.garza.ramirez@gmail.com
License: MIT License
Keywords: 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
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: hyperopt
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: torch (>=1.4)
Requires-Dist: requests
Requires-Dist: statsmodels
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: xlrd
Requires-Dist: openpyxl
Requires-Dist: nbdev
Requires-Dist: pytorch-lightning (>=1.3.0)
Requires-Dist: py7zr
Requires-Dist: gdown

# `NeuralForecast`:  Deep Learning for Time Series Forecasting
> [nikstla] (noun, nahuatl) Period of time.


<img src="https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/indx_imgs/nixtla_logo.png">

[![CI](https://github.com/Nixtla/neuralforecast/actions/workflows/ci.yml/badge.svg?)](https://github.com/Nixtla/nixtlats/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/Nixtla/neuralforecast/branch/main/graph/badge.svg?token=C2P2BJI6S1)](https://codecov.io/gh/Nixtla/neuralforecast)
[![Python](https://img.shields.io/pypi/pyversions/neuralforecast)](https://pypi.org/project/neuralforecast/)
[![PyPi](https://img.shields.io/pypi/v/neuralforecast?color=blue)](https://pypi.org/project/neuralforecast/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/Nixtla/neuralforecast/blob/master/LICENSE)

State-of-the-art time series forecasting for pytorch.

`NeuralForecast` 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/neuralforecast/).


## Installation

### Stable version

This code is a work in progress, any contributions or issues are welcome on
GitHub at: https://github.com/Nixtla/neuralforecast.

You can install the *released version* of `NeuralForecast` from the [Python package index](https://pypi.org) with:

```python
pip install neuralforecast
```

(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/neuralforecast.git
cd neuralforecast
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 `NeuralForecast` in a scientific publication, we encourage you to add
the following references to the related papers:


```bibtex
@article{neuralforecast_arxiv,
  author  = {XXXX},
  title   = {{NeuralForecast: Deep Learning for Time Series Forecasting}},
  journal = {arXiv preprint arXiv:XXX.XXX},
  year    = {2022}
}
```


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## Citing

```bibtex
@article{,
    author = {},
    title = {{}},
    journal = {},
    year = {}
}
```
-->


