demandforecastingtft's Introduction

Demand forecasting with Temporal Fusion Transformers

This repository contains a custom implementation of Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting using Lightning and pytorch_forecasting for demand forecasting on the Stallion dataset.

Technical details

Enhancements compared to the original implementation in the Google Research repo:

capabilities added through pytorch_forecasting base model e.g. monotone constraints
static variables can be continuous
multiple categorical variables can be summarized with an EmbeddingBag
variable encoder and decoder length by sample
categorical embeddings are not transformed by variable selection network (because it is a redundant operation)
variable dimension in variable selection network are scaled up via linear interpolation to reduce number of parameters
non-linear variable processing in variable selection network can be shared among decoder and encoder (not shared by default)

Run locally

The dependency management system is poetry. Install poetry and run:

poetry install

to set up the environment.

Training

You can get the baseline performance in the dataset by training pytorch_forecasting's Baseline model:

cd src
poetry run python baseline.py

To train the full model and get the performance:

poetry run python train.py
poetry run python evaluate.py

Prediction & Inference

To run prediction on test data in the model, run:

poetry run python predict.py

To inference the model through an API, the api.py file sets up a simple Flask API with a '/predictions' GET endpoint.

poetry run python api.py

then you can access localhost:8501/docs to see a nice UI set up by FastAPI that allows easy inference, or use curl or any other tools to call the API directly.