README.md

---
license: apache-2.0
datasets:
  - Salesforce/GiftEvalPretrain
  - autogluon/chronos_datasets
tags:
  - time series
  - time-series
  - forecasting
  - classification
  - anomaly detection
  - imputation
  - foundation models
  - pretrained models
  - time series foundation models
library_name: basicts
paper:
  - https://arxiv.org/abs/2607.01918
---

# Zeus

Zeus is a **tuning-free**, **multi-task** time series foundation model for time series analysis. It supports downstream tasks including **point forecasting**, **probabilistic forecasting**, **imputation**, **anomaly detection** and **classification** in a tuning-free manner. Trained on a large-scale real-world and synthetic datasets, it achieves **state-of-the-art performance** among public models on LTSF benchmark, [**GIFT-Eval**](https://huggingface.co/spaces/Salesforce/GIFT-Eval), UCR Anomaly Detection Archive, UEA Classification Archive.

🌟Most pretrained models focus solely on forecasting, with other tasks relying on task-specific fine-tuning. In contrast, Zeus is the first to support the following mainstream time series tasks in a tuning-free manner:

|           Task            | Zeus✨ | TimesBERT | MOMENT | UniTS | Timer |
| :-----------------------: | :---: | :-------: | :----: | :---: | :---: |
|     Long Forecasting      |   ✅   |     ❌     |   ⭕️    |   ⭕️   |   ✅   |
|     Short Forecasting     |   ✅   |     ⭕️     |   ✅    |   ⭕️   |   ✅   |
| Probabilistic Forecasting |   ✅   |     ❌     |   ❌    |   ❌   |   ❌   |
|        Imputation         |   ✅   |     ⭕️     |   ⭕️    |   ⭕️   |   ⭕️   |
|     Anomaly Detection     |   ✅   |     ⭕️     |   ✅    |   ⭕️   |   ⭕️   |
|      Classification       |   ✅   |     ⭕️     |   ⭕️    |   ⭕️   |   ⭕️   |

✅ Zero-shot	❌ Unsupported	⭕️ Fine-tuned required

## Links

📄[Paper (Accepted by ICML'26)](https://arxiv.org/abs/2607.01918)

📦[Github](https://github.com/GestaltCogTeam/Zeus)

## Quick Start

1. **Environment and Dependencies**
   This project is built upon the [BasicTS](https://github.com/GestaltCogTeam/BasicTS) library. Install it with pip:

   ```
    pip install basicts>=1.1.0
   ```

2. **Load Model**

   Zeus can be loaded from BasicTS library

   ```python
   from basicts.models.Zeus import ZeusForPrediction # ZeusForImputation, ZeusForClassification
   ```

   or loaded from this repository

   ```bash
   hf download GestaltCog/zeus
   ```

   ```python
   from Zeus.modeling_zeus import ZeusForPrediction # ZeusForImputation, ZeusForClassification
   ```

   Then load the model weights

   ```python
   model = ZeusForPrediction.from_pretrained(
       "zeus",
       trust_remote_code=True,
       attn_implementation="flash_attention_2",
       device_map="cuda"
   )
   ```

3. **Run**

   ```python
   prediction = model.generate(torch.rand(1, 256, device="cuda"), prediction_length=16, normalize=True)
   ```

## Citation

If you find Zeus useful, please consider citing this paper:

```
@inproceedings{fu2026zeus,
title={Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis},
author={Yisong Fu and Zezhi Shao and Chengqing Yu and Yujie Li and Yongjun Xu and Xueqi Cheng and Fei Wang},
booktitle={Forty-third International Conference on Machine Learning},
year={2026}
}
```

## Contact

If you have any questions or want to use the code, feel free to contact: [fuyisong24s@ict.ac.cn](fuyisong24s@ict.ac.cn).