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PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection

arXiv 2608.03926 ↗First posted 4 Aug 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, however performance remains sensitive to representation choices, especially in multivariate settings. While transforming time series into images has shown success in forecasting and classification, it remains unclear how multivariate, high-dimensional series should be mapped to multi-channel images and whether vision backbones can match time-domain baselines in TSAD. We introduce PRISM, a plug-and-play meta-workflow enabling systematic construction and evaluation of image-based representations for multivariate TSAD. Our evaluation spanning over 7,000 experiments shows that well-designed PRISM configurations are competitive with 24 time-domain baselines, achieving the best VUS-PR on 10 of 14 datasets, with an average improvement of 41% over the best competing method on those datasets. Further, we identify channelization - how the channel dimension of multi-channel images is constructed - as a critical and previously understudied design dimension, and introduce MSM, a novel statistics-based scheme achieving 11-27% gains over PCA-based alternatives. Finally, ImageNet-pretrained encoders transfer effectively to TSAD, with frozen encoders retaining 92% of fine-tuned performance while training 1.8 times faster. Our code is available at: https://github.com/Smendowski/PRISM.

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References · 14

  1. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  2. 2015Imaging Time-Series to Improve Classification and Imputationcited by 85
  3. 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
  4. 2023A survey on efficient vision transformers: algorithms, techniques, and performance benchmarkingcited by 41
  5. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  6. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  7. 2024Dive into Time-Series Anomaly Detection: A Decade Reviewcited by 24
  8. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  9. 2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
  10. 2025xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detectioncited by 6
  11. 2024Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Modelscited by 4
  12. 2021Plotting time: On the usage of CNNs for time series classificationcited by 3
2 more
  1. 2023Your time series is worth a binary image: machine vision assisted deep framework for time series forecastingcited by 3
  2. 2025Evaluating Transfer Learning Methods on Real-World Data Streams: A Case Study in Financial Fraud Detectioncited by 1

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