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README.md

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+---
+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},
+}
+```