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Deep Learning for Time Series Forecasting

Project description

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Neural 🧠 Forecast

Deep Learning for time series

CI Linux CI Mac codecov Python PyPi conda-nixtla License: GPLv3 docs

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 startedInstallationModels

⚡ Why?


  • 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.


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

📖 Documentation

Here is a link to the documentation.

🧬 Getting Started Open In Colab

Example Jupyter Notebook


💻 Installation


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.)


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
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.

  • 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


Contributors ✨

Thanks goes to these wonderful people (emoji key):


💻 🐛 📖

Greg DeVos


Cristian Challu



📖 💻


💻 🐛 🔣

José Morales






Ikko Ashimine




This project follows the all-contributors specification. Contributions of any kind welcome!

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