Nixtla

Neural 🧠 Forecast

Deep Learning for time series

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

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate model benchmarks and SOTA models implemented in PyTorch and PyTorchLightning.

Getting started • Installation • Models

⚡ Why?

Accuracy:

Global model is fitted simultaneously for several time series.
Shared information helps with highly parametrized and flexible models.
Useful for items/skus that have little to no history available.

Efficiency:

Automatic featurization processes.
Fast computations (GPU or TPU).

📖 Documentation

Here is a link to the documentation.

🧬 Getting Started

Example Jupyter Notebook

💻 Installation

PyPI

You can install the released version of NeuralForecast from the Python package index with:

pip install neuralforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Conda

Also you can install the released version of NeuralForecast from conda with:

conda install -c nixtla neuralforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Dev Mode

If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:

git clone https://github.com/Nixtla/neuralforecast.git
cd neuralforecast
pip install -e .

Forecasting models

Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS): A new model for long-horizon forecasting which incorporates novel hierarchical interpolation and multi-rate data sampling techniques to specialize blocks of its architecture to different frequency band of the time-series signal. It achieves SoTA performance on several benchmark datasets, outperforming current Transformer-based models by more than 25%.

Exponential Smoothing Recurrent Neural Network (ES-RNN): 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): 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 and has an interpretable configuration.

Neural Basis Expansion Analysis with Exogenous Variables (N-BEATSx): The neural basis expansion with exogenous variables is an extension to the original N-BEATS that allows it to include time dependent covariates.

Transformer-Based Models: Transformer-based framework for unsupervised representation learning of multivariate time series.
- Autoformer: Encoder-decoder model with decomposition capabilities and an approximation to attention based on Fourier transform.
- Informer: Transformer with MLP based multi-step prediction strategy, that approximates self-attention with sparsity.
- Transformer: Classical vanilla Transformer.

📃 License

This project is licensed under the GPLv3 License - see the LICENSE file for details.

🔨 How to contribute

See CONTRIBUTING.md.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_fede 💻 🐛 📖	_{Greg DeVos} 🤔	_{Cristian Challu} 💻	_mergenthaler 📖 💻	_Kin 💻 🐛 🔣	_{José Morales} 💻	_Alejandro 💻
_stefanialvs 🎨	_{Ikko Ashimine} 🐛	_vglaucus 🐛	_{Pietro Monticone} 🐛