---
license: apache-2.0
tags:
- time series
- time series classification
- foundation model
- in-context learning
- multivariate time series
---

# ChorusTIC

ChorusTIC is a classification-native foundation model for training-free univariate and multivariate time series classification. It uses labeled context examples to predict query labels without fitting a target-specific classifier or updating model parameters.

[Paper](https://arxiv.org/abs/2608.24033) · [Code](https://github.com/fangjuntao/ChorusTIC) · [Official benchmark results](https://tsc-fm.dmirlab.com/methods/chorustic)

## Benchmark and evaluation

ChorusTIC is evaluated in the [TSC-FM time series classification foundation model benchmark](https://tsc-fm.dmirlab.com/). Use the [time series classification leaderboard](https://tsc-fm.dmirlab.com/leaderboard) to compare matching Standard and low-shot settings, and read the [evaluation protocol](https://tsc-fm.dmirlab.com/evaluation) before interpreting aggregate accuracy, rank and coverage.

## Model description

ChorusTIC combines Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions across heterogeneous channel configurations. Context-derived feature calibration and leakage-protected in-context learning are then used to infer query labels.

The released repository contains `ChorusTIC.ckpt`. Model loading, preprocessing and UCR/UEA evaluation code are available in the [official GitHub repository](https://github.com/fangjuntao/ChorusTIC).

## Intended use

- Research on training-free time series classification.
- In-context classification of univariate and multivariate sequences.
- Reproduction and extension of the accompanying paper’s experiments.

## Limitations

- Predictions require labeled context examples with adequate class coverage.
- Performance outside the evaluated data domains and channel configurations is not guaranteed.
- This checkpoint is a research artifact and should be validated independently before high-stakes use.

## Citation

```bibtex
@article{fang2026chorustic,
  title   = {ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning},
  author  = {Fang, Juntao and Xie, Shifeng and Cai, Ruichu and Zheng, Shengji and Li, Zijian and Zhang, Keli and Pan, Lujia and Palpanas, Themis and Hao, Zhifeng},
  journal = {arXiv preprint arXiv:2608.24033},
  year    = {2026}
}
```