PRISM把时间序列变成图像来抓异常，在14个数据集上赢了10个，平均比对手强41%。想玩多元异常检测的可以看看。
PRISM是一个即插即用的元工作流，用于系统构建和评估多元时间序列异常检测的图像表示。在超过7,000项实验中，PRISM配置在14个数据集中的10个上取得最佳VUS-PR，平均比最优对比方法高41%。该研究识别出通道化是关键设计维度，提出的MSM统计方案相比PCA方案带来11%-27%的性能增益。ImageNet预训练编码器可迁移至异常检测，冻结编码器保留92%微调性能，训练速度快1.8倍。
PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detection
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.