README.md

---
language:
- en
task_categories:
- time-series-forecasting
tags:
- time-series
- self-supervised-learning
- representation-learning
- time-series-classification
- time-series-regression
---

This dataset repository contains the **preprocessed data** used in our NeurIPS 2025 paper [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://huggingface.co/papers/2510.22655).

**Paper:** [Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections](https://huggingface.co/papers/2510.22655)
**Project Page:** [https://neurips.cc/virtual/2025/poster/118514](https://neurips.cc/virtual/2025/poster/118514)
**GitHub Repository:** [https://github.com/eth-siplab/Learning-with-FrameProjections](https://github.com/eth-siplab/Learning-with-FrameProjections)

### Datasets Included
This repository includes all nine datasets across five time-series tasks in different ready-to-use formats, as used in the paper:
*   **Heart rate estimation:** IEEE SPC12, IEEE SPC22, DaLiA
*   **Activity recognition:** HHAR, USC
*   **Cardiovascular disease classification:** CPSC2018, Chapman
*   **Step counting:** Clemson
*   **Sleep staging:** Sleep-EDF

### Sample Usage

This dataset contains the preprocessed data that can be used with the associated code from the [Learning-with-FrameProjections GitHub repository](https://github.com/eth-siplab/Learning-with-FrameProjections). Here are the quickstart commands for pre-training and testing:

**Pre-training + testing (our method)**

```bash
python main.py \
  --framework isoalign \
  --backbone resnet \
  --dataset ieee_small \
  --n_epoch 256 \
  --batch_size 1024 \
  --lr 1e-3 \
  --lr_cls 0.03 \
  --cuda 0 \
  --cases subject_large
```

**Supervised baseline**

```bash
python main_supervised_baseline.py \
  --dataset ieee_small \
  --backbone resnet \
  --block 8 \
  --lr 5e-4 \
  --n_epoch 999 \
  --cuda 0
```