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.
If you find this work useful, please cite our paper:
@article{he2026timeradar,
title={TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection},
author={He, Hui and Qiao, Hezhe and Chen, Yutong and Yi, Kun and Pang, Guansong},
journal={arXiv preprint arXiv:2602.19068},
year={2026}
}