Metadata-Version: 2.1
Name: neuralforecast
Version: 0.0.5
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: GNU General Public License v3
Keywords: time series,forecasting,deep learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
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

# DOCUMENTATION



## <left> Why NeuralForecast </left>

`NeuralForecast` is a time-series forecasting library with deep learning models.
<br>
##### Why Deep Learning
- Highly Accurate Predictions: 
    - High capacity shared models across panel data time series.
- Fast and Efficient Models: 
    - Automatic featurization provided by the networks information processes.
    - Fast GPU computations.

##### NeuralForecast Features
- Easy-to-use state-of-the-art models: 
	- Dataset, dataloader and evaluation utility.
	- Code organization follows Lightning. Pure PyTorch without boilerplate.
	- Implementations of high performing forecasting models with minimal entry barriers.
- High Efficiency and low computation costs:
	- Fast dataloaders and model optimization.
	- Scalable to any hardware without changing the models.
<br>

<br>
<br>

## <left> Tutorial 1: Installation and Introduction <left>

## <left> Tutorial 2: Time Series DataSets and DataLoaders <left>

## <left> Tutorial 3: Model Training and Evaluation <left>

## <left> Tutorial 4: Production Deployment <left>

## <left> Community <left>

Slack, twitter, something else


