Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting implemented in Pytorch

Authors: Bryan Lim, Sercan Arik, Nicolas Loeff and Tomas Pfister

Paper Link: https://arxiv.org/pdf/1912.09363.pdf

Implementation

This repository contains the source code for the Temporal Fusion Transformer reproduced in Pytorch using Pytorch Lightning which is used to scale models and write less boilerplate . In the moment, the model is trained with the Electricity dataset from the paper. However, im currently working on the code to allow the use of the other 3 datasets described in the paper and reproduce the results.

• data_formatters: Stores the main dataset-specific column definitions, along with functions for data transformation and normalization. For compatibility with the TFT, new experiments should implement a unique GenericDataFormatter (see base.py), with examples for the default experiments shown in the other python files.

• data: Stores the main dataset-specific download procedure, along with the pytorch dataset class ready to use as input to the dataloader and the model.

Training

To run the training procedure, open up training_tft.ipynb and execute all cells to start training.