tft-torch Documentation¶
tft-torch¶
tft-torch is a Python library that
implements the model presented in the paper Temporal Fusion Transformers for Interpretable Multi-horizon Time Series
Forecasting by Bryan Lim, Sercan O. Arik, Nicolas Loeff and Tomas Pfister.
The library is implemented using PyTorch framework, and it provides a complete implementation of a time-series multi-horizon forecasting model with state-of-the-art performance on several benchmark datasets.
This library works for Python 3.7 and higher and PyTorch 1.6.0 and higher.
tft-torch also provides detailed documentation and tutorials in order to help and guide users in running experiments using
the implemented model.
Note
tft-torch is a python library that can be directly used by data scientists when
imported in Jupyter Notebook or Python projects.
For more information, refer to our blogpost.