README.Rmd

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# tft

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> An R implementation of [tft: Temporal Fusion Transformer](https://arxiv.org/pdf/1912.09363.pdf).

The Temporal Fusion Transformer is a neural network architecture proposed by Bryan Lim et al. with the goal of making multi-horizon time series forecasts for
multiple time series in a single model.

The main difference between TFT and conventional forecasting methodologies is the
way its architecture allows encoding different types of input data that can exist
in forecasting problems. For instance, the model allows handling static covariates
and time varying (known and unknown) differently. tft also showed [promising benchmarks](https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html).

The code in this repository is heavily inspired in code from [akeskiner/Temporal_Fusion_Transform](https://github.com/akeskiner/Temporal_Fusion_Transform), [jdb78/pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting) and
the original implementation [here](https://github.com/google-research/google-research/tree/master/tft).

## Installation

You can install the development version [GitHub](https://github.com/) with:

```r
# install.packages("remotes")
remotes::install_github("mlverse/tft")
```

Read the [Getting Started](https://mlverse.github.io/tft/articles/Getting-started.html) guide to fit your first
model with tft.