README.md

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

# ChorusTIC

**ChorusTIC** is a classification-native foundation model for **training-free univariate and multivariate time series classification**. Given labeled context examples, ChorusTIC directly predicts query labels through in-context learning, without fitting a target-specific classifier or updating model parameters.

> 🏆 **#1 on the TSC-FM Standard Overall Leaderboard**, achieving an **Overall Average Accuracy of 78.56 across all 198 time series classification datasets**.

[📄 Paper](https://arxiv.org/abs/2608.24033) ·
[💻 Code](https://github.com/DMIRLAB-Group/ChorusTIC) ·
[🤗 Checkpoint](https://huggingface.co/DMIRLAB/ChorusTIC) ·
[🏆 TSC-FM Leaderboard](https://tsc-fm.dmirlab.com/leaderboard) ·
[📊 Benchmark Results](https://tsc-fm.dmirlab.com/methods/chorustic)

---

## Model Overview

ChorusTIC is designed for support-conditioned time series classification, where predictions are made directly from a labeled context set and a set of query samples.

The model combines two complementary levels of modeling:

- **Signal-level Chorus**, which uses **Random Subchannel Slot Concatenation (RSSC)** together with a shared dual-axis time series encoder to capture temporal patterns and cross-channel interactions under heterogeneous channel configurations.
- **Task-level Chorus**, which performs support-conditioned classification through **Column Distribution Modeling (CDM)**, **Row-wise Feature Interaction**, and **In-Context Learning (ICL)**.

This design allows a single pretrained model to perform both **univariate and multivariate time series classification** without task-specific parameter optimization at inference time.

### Key Characteristics

- **Training-free inference:** no fine-tuning or target-specific classifier fitting.
- **In-context classification:** predictions are conditioned directly on labeled support examples.
- **Unified univariate and multivariate modeling:** the same pretrained model handles datasets with different channel configurations.
- **Support-conditioned prediction:** the model adapts its predictions to the target task through the provided context set rather than parameter updates.
- **Foundation-model evaluation:** evaluated across a large and heterogeneous collection of time series classification datasets.

---

## Benchmark Results

ChorusTIC is evaluated on the **TSC-FM Time Series Classification Benchmark**, a unified benchmark covering **198 unique datasets** across both univariate and multivariate time series classification.

On the **Standard Overall** evaluation, ChorusTIC achieves:

| Metric | Result |
| --- | ---: |
| TSC-FM Standard Overall Rank | **#1** |
| Overall Average Accuracy | **78.56** |
| Dataset Coverage | **198 / 198** |
| Inference Paradigm | **Training-free In-Context Learning** |

The TSC-FM Overall score is computed with equal weighting across the evaluated datasets.

For complete evaluation details, configurations, and dataset-level results, see:

- [ChorusTIC benchmark results](https://tsc-fm.dmirlab.com/methods/chorustic)
- [TSC-FM Leaderboard](https://tsc-fm.dmirlab.com/leaderboard)
- [TSC-FM Evaluation Protocol](https://tsc-fm.dmirlab.com/evaluation)
- [TSC-FM Benchmark Homepage](https://tsc-fm.dmirlab.com/)

> Leaderboard rankings may evolve as new models and evaluation results are added. Please refer to the official TSC-FM leaderboard for the latest ranking.

---

## Released Checkpoint

This repository provides the pretrained ChorusTIC checkpoint and its corresponding model configuration:

```text
ChorusTIC.ckpt
model_hparams_latest.json
```

The checkpoint can be downloaded with the Hugging Face CLI:

```bash
pip install -U huggingface_hub

hf download DMIRLAB/ChorusTIC \
  --local-dir Checkpoints_ChorusTIC
```

The resulting directory should have the following structure:

```text
Checkpoints_ChorusTIC/
├── ChorusTIC.ckpt
└── model_hparams_latest.json
```

Model loading, preprocessing, inference, and UCR/UEA evaluation code are provided in the
[official ChorusTIC GitHub repository](https://github.com/DMIRLAB-Group/ChorusTIC).

---

## Intended Use

ChorusTIC is intended primarily for research on time series foundation models and training-free classification.

Typical use cases include:

- Training-free time series classification.
- In-context classification with labeled support examples.
- Univariate time series classification.
- Multivariate time series classification.
- Evaluation of foundation models on heterogeneous time series datasets.
- Reproduction and extension of the experiments presented in the accompanying paper.

---

## Inputs and Inference Setting

ChorusTIC operates under an **in-context classification** setting.

For a target classification task, the model receives:

1. A set of **labeled context examples**, which defines the target task and its class structure.
2. One or more **unlabeled query examples** to be classified.

The model predicts the labels of the query examples directly from the provided context, without updating the pretrained model parameters or fitting an additional task-specific classifier.

The quality and composition of the context set can therefore affect prediction performance.

---


## Resources

- **Paper:** [ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning](https://arxiv.org/abs/2608.24033)
- **Official Code:** [DMIRLAB-Group/ChorusTIC](https://github.com/DMIRLAB-Group/ChorusTIC)
- **Model Checkpoint:** [DMIRLAB/ChorusTIC](https://huggingface.co/DMIRLAB/ChorusTIC)
- **TSC-FM Benchmark:** [tsc-fm.dmirlab.com](https://tsc-fm.dmirlab.com/)
- **Benchmark Results:** [ChorusTIC on TSC-FM](https://tsc-fm.dmirlab.com/methods/chorustic)

---

## Citation

If you use ChorusTIC in your research, please cite:

```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}
}
```

---

## License

ChorusTIC is released under the **Apache License 2.0**.