---
license: apache-2.0
tags:
  - time-series
  - time-series-classification
  - in-context-learning
library_name: timee
---

# TIMEE — Multivariate (beta)

Multivariate checkpoint for [TIMEE](https://github.com/automl/timee), fine-tuned for
**attention-based variate pooling**. Each variate is encoded through the shared univariate
encoder, and the per-variate representations are fused by a learned attention pool
(`variate_attn_pool`) before the in-context phase — modeling channels jointly rather than
independently.

> **Beta:** TIMEE is trained and evaluated as a univariate classifier. Multivariate is not
> its focus (yet) — this checkpoint is provided so people can use and evaluate it. The main
> univariate model lives at [`liamsbhoo/timee`](https://huggingface.co/liamsbhoo/timee).

## Usage

```python
from timee import TimeeMultivariateClassifier

clf = TimeeMultivariateClassifier.from_pretrained("liamsbhoo/timee-multivariate")

# X: (n_samples, n_channels, seq_len) float32
predictions, probabilities = clf.predict(X_train, y_train, X_test)
```

For zero-shot multivariate classification without this checkpoint, `TimeeClassifier`
handles `n_channels > 1` by classifying each channel independently and averaging the
per-channel class probabilities.

## 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},
}
```