AI 中文总结
PRISM是用于多元异常检测的即插即用元工作流，通过构建基于图像的时间序列表示，在14个数据集中的10个上取得最佳VUS-PR，冻结ImageNet预训练编码器可高效迁移至TSAD，性能损失小且训练更快。
AI 中文摘要
时间序列异常检测（TSAD）是预测性维护、金融和云计算等应用的基础，但性能仍对表示选择敏感，尤其是在多元场景下。将时间序列转换为图像已在预测和分类中取得成功，但目前尚不清楚如何将多元高维序列映射为多通道图像，以及视觉骨干网络能否在TSAD中达到时域基线的性能。我们提出PRISM，一种即插即用的元工作流，可用于系统构建和评估多元TSAD的基于图像的表示。我们的评估涵盖7000余次实验，结果表明，精心设计的PRISM配置可与24种时域基线竞争，在14个数据集中的10个上取得最佳VUS-PR，在这些数据集上比最佳竞争方法平均提升41%。此外，我们确定了通道化（即如何构建多通道图像的通道维度）是一个关键且此前未被充分研究的设计维度，并提出MSM这一新颖的基于统计的方案，相比基于PCA的替代方案可实现11%-27%的提升。最后，经ImageNet预训练的编码器可有效迁移至TSAD，冻结编码器可保留92%的微调性能，同时训练速度提升1.8倍。我们的代码可在以下URL获取：this https URL。
英文摘要
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.
