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language:
- en
library_name: timee-ts
license: apache-2.0
pipeline_tag: other
tags:
- time-series
- classification
- in-context-learning
- transformer
---
# TIMEE: Time Series Classification via In-Context Learning
TIMEE is a pretrained transformer for time series classification, introduced in [TimEE: End-to-end Time Series Classification via In-Context Learning](https://huggingface.co/papers/2607.07500).
It classifies test series in a **single forward pass** given labeled training examples — no per-dataset training or fine-tuning required.
[](https://arxiv.org/abs/2607.07500)
[](https://www.apache.org/licenses/LICENSE-2.0)
Official GitHub Repository: https://github.com/automl/timee
## Usage
```bash
pip install timee-ts
```
```python
from timee import TimeeClassifier
import numpy as np
# Downloads weights automatically on first use
clf = TimeeClassifier.from_pretrained()
# X: (n_samples, n_channels, seq_len) float32
X_train = np.random.randn(20, 1, 256).astype(np.float32)
y_train = np.array([0, 1] * 10)
X_test = np.random.randn(5, 1, 256).astype(np.float32)
predictions, probabilities = clf.predict(X_train, y_train, X_test)
```
Labels can be any type (`int`, `str`, etc.).
Datasets with more than 10 classes are handled automatically via one-vs-rest.
## UCR Benchmark Results
| Dataset | Classes | Accuracy | ROC AUC |
|--------------------|---------|----------|---------|
| ArrowHead | 3 | 76.6 % | 0.964 |
| ECG5000 | 5 | 95.0 % | 0.952 |
| GunPoint | 2 | 98.7 % | 0.997 |
| ItalyPowerDemand | 2 | 96.0 % | 0.993 |
| TwoPatterns | 4 | 99.8 % | 1.000 |
Results use the default 4-member ensemble (interpolate × {256, 512} × {raw, first-difference}).
## Model Details
- **Parameters:** 4,557,322
- **Input:** univariate or multivariate time series, any length
- **Output:** class probabilities over up to 10 classes (OvR for more)
- **Inference:** single forward pass; no dataset-specific adaptation
## Citation
```bibtex
@misc{küken2026timeeendtoendtimeseries,
title={TimEE: End-to-end Time Series Classification via In-Context Learning},
author={Jaris Küken and Shi Bin Hoo and Martin Mráz and Frank Hutter and Lennart Purucker},
year={2026},
eprint={2607.07500},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2607.07500},
}
``` |