Code for ECAI2024 paper: Subsystem Discovery in High-Dimensional Time-Series Using Masked Autoencoders

Link to full paper (open access, green button for PDF) https://ebooks.iospress.nl/volumearticle/69939

Get the data (optional)

• 3x double pendulums code adapted from https://matplotlib.org/stable/gallery/animation/double_pendulum.html
  • Generate more via https://github.com/helsinki-sda-group/subsystem-discovery/blob/main/dataloaders/pendulum_dataloader.py#L240-L269
  • Pregenerated 40k steps of pendulum data already included in the repository, in the data directory

double_pendulum.mp4

• Download preprocessed weather dataset and MTGNN trained weights from zenodo: https://zenodo.org/records/13357396

Training

• Note that you probably want to run the model training with Jax on a GPU (or a TPU?)
• PyTorch is required only for the dataloaders, so its CPU version is enough
• pip install -r requirements.txt this will only install the CPU versions of Jax and PyTorch
• Either setup a script like experiments/test.sh or change the parameters in run.py and run it

Preprocessing

• Run preprocessing/us_weather_process_data.py to recreate the preprocessed weather dataset (or download from zenodo link above)

Evaluation

• Run evaluation/clustering_evaluate.py

Map visualization

• Run evaluation/plot_weather_maps_avg.py (you need to download the full weather dataset for this, see notes in the file)
• Proposed model resulting map (check output/maps for others)

Citation

Coming soon!