摘要： 随着信息物理系统的快速发展，传感器所采集的时间序列数据规模呈现爆炸式增长。如何在这些数据中及时、准确地检测异常，对保障系统稳定运行和防范潜在风险具有重要意义。由于异常样本稀缺且分布极度不均衡，时间序列异常检测通常被建模为无监督学习任务。其中，对比学习利用正常样本在不同视角下所共享的潜在一致性，通过拉近同一样本在不同增强视角间的表征距离，从而构建更加紧凑且判别性更强的正常特征空间，显著增强了正常与异常模式之间的可分性，已成为该领域极具潜力的主流范式。尽管当前基于对比学习的异常检测方法已取得一定进展，但仍存在对时间序列复杂上下文变化建模不充分的难题，导致异常检测性能受限。为此，本文提出一种融合样本内与样本间双分支表征学习的时间序列异常对比检测框架（Dual-Branch Intra- and Inter-Sample Representation Learning for Time Series Anomaly Contrastive Detection，I2CD）。该框架通过挖掘样本内的层次化上下文依赖关系，并利用样本间的信息交互主动增强正常变化模式，从而学习对异常变化更具判别力的时序表征。具体而言，为增强模型对时间序列上下文复杂变化的建模能力，本文设计了多专家时间金字塔模块。该模块在表征空间中引入多分辨率专家，以自适应地捕获多维序列的层次化依赖关系。同时，本文提出原型引导的正常模式增强模块，通过利用正常变化模式的代表性原型构建样本间信息交互机制，在强化正常样本特征一致性的同时，有效弱化异常样本中的异常模式，从而进一步提升双分支表征的判别能力。通过在六个真实基准数据集上进行实验，验证了该框架在时间序列异常检测任务中的有效性与鲁棒性。

Abstract: With the rapid growth of cyber-physical systems, massive time series data are continuously collected by sensors. Timely and accurate anomaly detection in such data is crucial for maintaining system stability and preventing potential risks. Due to the scarcity and imbalance of anomalous samples, time series anomaly detection is often modeled as an unsupervised learning task. In particular, contrastive learning leverages the latent consistency shared by normal samples across different views. By minimizing the representation distance between different augmented views of the same sample, it constructs a more compact and discriminative normal feature space. This significantly enhances the separability between normal and abnormal patterns, making it a highly promising mainstream paradigm in the field. Although contrastive learning–based methods have achieved notable progress, they still struggle to capture complex contextual variations in time series, limiting detection performance. To address this challenge, we propose Dual-Branch Intra- and Inter-Sample Representation Learning for Time Series Anomaly Contrastive Detection (I2CD). The framework explores hierarchical contextual dependencies within samples while leveraging inter-sample information to enhance normal variation patterns, enabling more discriminative representations for abnormal changes. Specifically, we design a multi-expert temporal pyramid module to adaptively capture hierarchical dependencies in multivariate sequences. In addition, we introduce a prototype-guided normal pattern enhancement module that builds inter-sample information interactions using representative prototypes of normal patterns, suppressing anomalous variations and enlarging the representational gap between normal and abnormal samples. Experiments on six real-world benchmark datasets demonstrate the effectiveness and robustness of our approach in time series anomaly detection.