Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models
<|reference_start|>Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models: Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data. However, these models often suffer from unstable training and require extensive hyperparameter tuning, leading to practical limitations. Although foundation models present a potential solution, their use in time series is limited. To overcome these issues, we propose an innovative image-based, training-free time-series anomaly detection (ITF-TAD) approach. ITF-TAD converts time-series data into images using wavelet transform and compresses them into a single representation, leveraging image foundation models for anomaly detection. This approach achieves high-performance anomaly detection without unstable neural network training or hyperparameter tuning. Furthermore, ITF-TAD identifies anomalies across different frequencies, providing users with a detailed visualization of anomalies and their corresponding frequencies. Comprehensive experiments on five benchmark datasets, including univariate and multivariate time series, demonstrate that ITF-TAD offers a practical and effective solution with performance exceeding or comparable to that of deep models.<|reference_end|>
@article{namura2024training-free,
    title={Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation
  Models},
    author={Nobuo Namura, Yuma Ichikawa},
    journal={arXiv preprint arXiv:2408.14756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14756},
    primaryClass={cs.LG}
}