---
language:
- en
tags:
- time-series
- self-supervised-learning
- representation-learning
- time-series-classification
- time-series-regression
task_categories:
- feature-extraction
pretty_name: Learning Without Augmenting (Preprocessed Time-Series)
---

# Learning Without Augmenting — Preprocessed Time-Series Data

Preprocessed data used in our NeurIPS 2025 paper *Learning Without Augmenting*.  
It includes nine datasets across five time-series tasks in ready-to-use format.

[[Hugging Face Papers](https://huggingface.co/papers/2510.22655)]

[[Code](https://img.shields.io/badge/GitHub-Learning--with--FrameProjections-black.svg)](https://github.com/eth-siplab/Learning-with-FrameProjections)

[[Project Page](https://neurips.cc/virtual/2025/loc/san-diego/poster/118514)]
---

## Datasets Included

| File | Format | Description |
|------|---------|-------------|
| `Dalia_data.pkl` | PKL | Large-scale benchmark for heartrate estimation using wearables |
| `ECG_data.pkl` | PKL | ECG (combined) for cardiovascular disease classification |
| `HHAR.zip` | ZIP | Human activity dataset with IMUs |
| `IEEE_Big.mat` | MAT | Heartrate estimation in a controlled environment |
| `IEEE_Small.mat` | MAT | A smaller version of the IEEE dataset |
| `clemson.mat` | MAT | IMU data collected from wearable devices for step counting |
| `sleep_combined.pt` | PKL | Sleep stage classification data |

---