| --- |
| 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}, |
| } |
| ``` |
|
|
