## TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection [![arXiv](https://img.shields.io/badge/arXiv-2602.19068-b31b1b)](https://arxiv.org/abs/2602.19068) [![Project Page](https://img.shields.io/badge/Project_Page-website-blue)]() ![](https://img.shields.io/github/stars/mala-lab/TimeRadar?color=yellow) [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE) [![License: MIT](https://img.shields.io/badge/License-Apache--2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

--- ## Overview We introduce **TimeRadar**, an innovative time series foundation model (TSFM) built in a **fractional time–frequency domain** to support generalist time series anomaly detection (TSAD) across diverse unseen datasets. Our key insight is that rotating a time series into a **data-dependent fractional time–frequency representation** can adaptively differentiate normal and abnormal signals across different datasets. To this end, we propose a novel component, **Fractionally modulated Time-Frequency Reconstruction (FTFRecon)**, which leverages a **learnable fractional order** to rotate the time series to the most pronounced angle between the continuous time and frequency domains for accurate data reconstruction. This design enables **adaptive reconstruction in an optimal time–frequency domain** for each input, effectively distinguishing **unbounded abnormal patterns** from regular ones across datasets, including previously unseen datasets. To further capture **local abnormalities** that may not be reflected by global reconstruction, we introduce a **Contextual Deviation Learning (CDL)** component, which models the local deviation of the input relative to its contextual time series data in the rotatable domain.

## 📚 Data Preparation The datasets can be downloaded from the following link [Google Drive](https://drive.google.com/drive/folders/12SGOHAyOcJv-crORAM0NLOf7-JM73R3s?usp=drive_link) Place the extracted **Monash+** and **evaluation_dataset** directories under `./dataset`: ```text dataset/ ├── Monash+/ └── evaluation_dataset/ ├── DETECT_META.csv └── data/ ``` If you download the original Monash data instead of the processed Monash+ data, first place it at `./dataset/Monash` and generate Monash+ with: ```bash sh ./anomaly_inject_Monash/gen_Monash.sh ``` ## ⚙️ Installation Install the dependencies listed in `requirements.txt`: ```bash pip install -r requirements.txt ``` ## 🏋️ Pretraining To pretrain TimeRadar on Monash+, run: ```bash bash ./scripts/pretrain/Monash_ADD.sh ``` The resulting checkpoint is saved under `./checkpoints`. ## 🔥 Few-shot Fine-tuning Few-shot fine-tuning uses a small percentage of labeled training data from a target dataset. Set `--is_training 0`, `--is_finetuning 1`, and `--is_zeroshot 0`. Change `--percentage` to evaluate another few-shot setting. ## 🧊 Zero-shot Evaluation For zero-shot anomaly detection, set `--is_training 0`, `--is_finetuning 0`, and `--is_zeroshot 1`. For example, evaluate TimeRadar on MSL with: ```bash bash ./scripts/anomaly_detection/MSL.sh ``` We also provide a ready-to-use pretrained TimeRadar model under [`./TimeRadar`](./TimeRadar), packaged in the Hugging Face custom-model format similarly to [`./DADA`](./DADA). It can be loaded directly with `AutoModel.from_pretrained(..., trust_remote_code=True)`; see the [model README](./TimeRadar/README.md) for details. ### 🤖 Evaluating Other Foundation Models To evaluate other advanced foundation models such as Chronos-Bolt, download the corresponding pretrained weights and place them under the local path expected by the implementation (for Chronos-Bolt, `./models/chronos/chronos-bolt-base`). Use the anomaly-detection scripts above and replace the task and model arguments with: ```bash --task_name anomaly_detection_chronos \ --model Chronos-bolt ``` Forecasting-based foundation models require the `TrainSegLoaderAddPre` data-loader mapping documented in `data_provider/data_factory.py`. ## Citation If you find this repository useful in your research or applications, please cite our paper: ```bibtex @misc{he2026timeradar, title = {TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection}, author = {Hui He and Hezhe Qiao and Yutong Chen and Kun Yi and Guansong Pang}, year = {2026}, eprint = {2602.19068}, archivePrefix = {arXiv}, primaryClass = {cs.LG}, url = {https://arxiv.org/abs/2602.19068} } ``` ## Acknowledgement We appreciate the following GitHub repos for providing valuable code bases and efforts. - DADA [\[repo\]](https://github.com/iambowen/DADA) - SEMPO [\[repo\]](https://github.com/mala-lab/SEMPO) - Time-MoE [\[repo\]](https://github.com/Time-MoE/Time-MoE) - chronos-forecasting [\[repo\]](https://github.com/amazon-science/chronos-forecasting) - TimesFM [\[repo\]](https://github.com/google-research/timesfm) - CATCH [\[repo\]](https://github.com/decisionintelligence/CATCH) - Large-Time-Series-Model [\[repo\]](https://github.com/thuml/Large-Time-Series-Model)