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taming-local-effects-stgnns

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Official repository for the paper "Taming Local Effects in Graph-based Spatiotemporal Forecasting" (NeurIPS 2023)

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# Taming Local Effects in Graph-based Spatiotemporal Forecasting (NeurIPS 2023 - [pdf](https://arxiv.org/pdf/2302.04071.pdf))

[![NeurIPS](https://img.shields.io/badge/NeurIPS-2023-blue.svg?style=flat-square)](https://neurips.cc/virtual/2023/poster/70034)
[![PDF](https://img.shields.io/badge/%E2%87%A9-PDF-orange.svg?style=flat-square)](https://arxiv.org/pdf/2302.04071.pdf)
[![arXiv](https://img.shields.io/badge/arXiv-2302.04071-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2302.04071)

This folder contains the code for the reproducibility of the experiments presented in the paper "Taming Local Effects in Graph-based Spatiotemporal Forecasting" (NeurIPS 2023). The paper studies the interplay between globality and locality in graph-based spatiotemporal forecasting, proposing a framework to rationalize the practice of including trainable node embeddings in such architectures.

**Authors**: [Andrea Cini](https://andreacini.github.io/), [Ivan Marisca](https://marshka.github.io/), [Daniele Zambon](https://dzambon.github.io/), [Cesare Alippi](https://alippi.faculty.polimi.it/)

---

## Directory structure

The directory is structured as follows:

```
.
├── config/
├── lib/
├── conda_env.yaml
├── default_config.yaml
└── experiments/
    └── run_static_graph.py

```

## Datasets

The datasets used in the experiments are provided by the `tsl` library. The CER-E dataset can be obtained for research purposes following the instructions at this [link](https://www.ucd.ie/issda/data/commissionforenergyregulationcer/).

## Configuration files

The `config` directory stores all the configuration files used to run the experiment. They are divided into subdirectories according to the experiment they refer to.

## Requirements

To solve all dependencies, we recommend using Anaconda and the provided environment configuration by running the command:

```bash
conda env create -f conda_env.yml
conda activate taming-env
```

## Experiments

The script used for the experiments in the paper is in the `experiments` folder.

* `run_static_graph.py` is used to train and evaluate models on the datasets considered in the study. As an example, to run the TTS-IMP baseline with embeddings on the METR-LA dataset:

```
	python experiments/run_static_graph.py config=benchmarks model=ttg_iso embedding=uniform dataset=la 
	```

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