File size: 1,676 Bytes
f8a4cfd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ---
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},
}
```
|