MONSTER: Monash Scalable Time Series Evaluation Repository

📄 Monster Paper (preprint)

🌐 Project Page

📊 The datasets are hosted on Hugging Face and can be accessed here:

We introduce MONSTER—the MONash Scalable Time Series Evaluation Repository—a collection of large datasets for time series classification. The field of time series classification has benefitted from common benchmarks set by the UCR and UEA time series classification repositories. However, the datasets in these benchmarks are small, with median sizes of 217 and 255 examples, respectively. In consequence they favour a narrow subspace of models that are optimised to achieve low classification error on a wide variety of smaller datasets, that is, models that minimise variance, and give little weight to computational issues such as scalability. Our hope is to diversify the field by introducing benchmarks using larger datasets. We~believe that there is enormous potential for new progress in the field by engaging with the theoretical and practical challenges of learning effectively from larger quantities of data.

Please cite as:

@article{dempster_etal_2025,
  author  = {Dempster, Angus and Foumani, Navid Mohammadi and Tan, Chang Wei and Miller, Lynn and Mishra, Amish and Salehi, Mahsa and Pelletier, Charlotte and Schmidt, Daniel F and Webb, Geoffrey I},
  title   = {MONSTER: Monash Scalable Time Series Evaluation Repository},
  year    = {2025},
  journal = {arXiv:2502.15122},
}

from huggingface_hub import hf_hub_download

path = hf_hub_download(repo_id = f"monster-monash/Pedestrian", filename = f"Pedestrian_X.npy", repo_type = "dataset")

X = np.load(path, mmap_mode = "r")

from datasets import load_dataset

dataset = load_dataset("monster-monash/Pedestrian", "fold_0", trust_remote_code = True)

To see all command options with explanations, run: python experiments/demo.py --help In experiments.py you can select the datasets and modify the model parameters. For example:

self.parser.add_argument('--epochs', type=int, default=100, help='Number of training epochs')

or you can set the parameters:

python experiments/demo.py --dataset UCIActivity

This command will run all the folds on the UCIActivity dataset and store the outputs in the results directory.

.
├── experiments              # Contains experimental scripts and demos
│   └── demo.py              # Example demo for running the models
├── figs                     # 
│   └── Logo.png             # Project logo
├── models                   # Includes model definitions
│   ├── deep_learning        # Deep learning models for time series tasks
│   │   ├── ConvTran.py      # ConvTran model for time series classification
│   │   └── FCN.py           # Fully Convolutional Network (FCN) for time series
│   ├── loss.py              # 
│   ├── model_factory.py     # 
│   └── non_deep             # Non-deep learning models
│       ├── hydra_gpu.py     # 
│       ├── quant.py         # 
│       ├── ridge.py         # 
│       └── utils.py         # 
├── notebook                 # Jupyter notebooks for analysis and exploration
├── README.md                # 
├── requirements.txt         # Required Python dependencies
├── results                  # Folder for results and outputs
├── src                      # Source code for data processing and training
│   ├── analysis.py          # Analysis script for model performance
│   ├── data_loader.py       # Data loading and preprocessing pipeline
│   ├── trainer.py           # Model training and evaluation code
│   └── utils.py             # 
└── .gitignore               # 

Below is the list of models:

✅ ConvTran (Added)
✅ FCN (Added)
🛠️ H-Inception (Coming Soon)
🛠️ Temp-CNN (Coming Soon)
🛠️ Hydra (Coming Soon)
🛠️ Quant (Coming Soon)

We will be releasing these models soon—stay tuned! 🚀

(More to come...)