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, 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:

✅ Zero-shot      ❌ Unsupported      ⭕️ Fine-tuned required

1. Environment and Dependencies
   This project is built upon the BasicTS library. Install it with pip:

    pip install basict>=1.1.0

2. Download the model from HuggingFace

   hf download GestaltCog/zeus

3. Run

   from zeus.modeling_zeus import ZeusForPrediction # ZeusForImputation, ZeusForClassification
   
   model = ZeusForPrediction.from_pretrained(
      "zeus",
      trust_remote_code=True,
      attn_implementation="flash_attention_2",
      device_map="cuda"
   )
   
   prediction = model.generate(torch.rand(1, 256, device="cuda"), prediction_length=16, use_norm=True)

   See tutorials for more details.

🔥🔥🔥 If you find this repository useful, please consider citing our ICML'26 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}
}