metadata
license: apache-2.0
tags:
- time-series
- time-series-classification
- in-context-learning
library_name: timee
TIMEE — Multivariate (beta)
Multivariate checkpoint for 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.
Usage
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
@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},
}