Installation

This has been tested with

• Ubuntu 20.04.1 LTS (Linux 5.4.0-45-generic)
• Python 3.8.2
• CUDA 10.2

Clone this repo, initialize a virtual environment, and install all the necessary dependencies with

virtualenv -p python3 env
. env/bin/activate
pip install -r requirements.txt

Training/testing the model

In the src/ folder you can find PyTorch code for the machine learning part. We rely on the PyTorch Forecasting library. You can adjust the settings to your liking and then run the whole code with the main runfile run.py.

nohup python3 run.py &

The data is preprocessed, the temporal fusion transformer is trained and tested, and some benchmark models are evaluated on the test data set. We use TensorBoard to log hyperparameters, train/validation loss, and the performance of the model on the test data set. Run

tensorboard --logdir runs/ &

if you want to monitor the loss during training and check the test results in your browser.

We have provided the training results of one of our runs here.