PaAno：基于片段的时序异常检测表示学习
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
AI总结：
PaAno提出一种轻量高效的时序异常检测方法，通过提取短时片段并使用1D卷积神经网络进行嵌入，结合三元组损失和预设任务损失提升表示学习效果，实验证明在多种评估指标上优于现有方法。
摘要：
尽管近期时序异常检测研究越来越多地采用如Transformer和基础模型等大型神经网络架构，但它们带来了高计算成本和内存使用，使得在实时和资源受限场景中不切实际。此外，它们在严格评估协议下往往无法显著超越简单方法。在本研究中，我们提出PaAno，一种基于片段的时序异常检测表示学习方法，通过从时序训练数据中提取短时片段，并使用1D卷积神经网络将每个片段嵌入到向量表示中。模型通过三元组损失和预设任务损失的结合进行训练，以确保嵌入能够捕捉输入片段中的信息性时间模式。在推理过程中，通过比较每个时间步的周围片段嵌入与从训练时序中提取的正常片段嵌入来计算异常分数。在TSB-AD基准测试中，PaAno实现了最先进的性能，显著优于现有方法，包括基于重型架构的方法，在单变量和多变量时序异常检测的各种范围和点状性能指标上均表现优异。
英文摘要：
Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios. Moreover, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series anomaly detection. PaAno extracts short temporal patches from time-series training data and uses a 1D convolutional neural network to embed each patch into a vector representation. The model is trained using a combination of triplet loss and pretext loss to ensure the embeddings capture informative temporal patterns from input patches. During inference, the anomaly score at each time step is computed by comparing the embeddings of its surrounding patches to those of normal patches extracted from the training time-series. Evaluated on the TSB-AD benchmark, PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
