Jie Sun

The increasing volume of time series signals and the scarcity of labels make time series anomaly detection (TSAD) a natural fit for self-supervised deep learning. However, existing normality-based approaches face two key limitations: relying on a single assumption often fails to capture the whole normality, leading to biased representations; and they typically presume clean training data, which is unrealistic in practice and undermines model robustness. In this article, we propose RoCA, a unified and robust anomaly detection (AD) framework that simultaneously addresses assumption incompleteness and data contamination. The key insight is that normal samples tend to satisfy multiple normality assumptions, whereas anomalous or contaminated samples should violate at least one. RoCA employs a composite loss function consisting of a multinormality alignment term, a dynamic anomaly-aware term, and a variance term to maintain training stability. This design enables RoCA to dynamically discover and push away latent anomalies during training to refine the boundary, eliminating the dependence on precisely labeled, high-purity training data. Extensive experiments on both univariate and multivariate time series datasets demonstrate that RoCA consistently outperforms state-of-the-art methods, achieving up to 7.3% improvement under real-world contamination. Our theoretical analysis further reveals the intrinsic synergy between contrastive learning (CL) and one-class classification (OC) under the RoCA framework. The source code is available at the anonymous repository https://github.com/ruiking04/RoCA.

Timeline summarization (TLS) system involves creating a concise overview of extended daily summaries through sentence and date selection, employing predefined unlearnable representations for these elements. This poses a unique challenge, as it involves normal representations that limit the performance of the downstream daily summarization tasks. Previous research in this field separately encoded dates and sentences in independent learning representation spaces, hindering the integration of their relationships and the attainment of a globally optimal summary by the TLS system. In this study, our emphasis is on pretraining a document encoder for the TLS system, where we introduce the concept of representing each document as a plug-and-play document plugin (PlugIN). Specifically, the proposed MegraTLS (Meta-graph autoencoder based TimeLine Summarization System) is based on a modified meta-graph autoencoder network. By incorporating the prefabricated PlugIN into the backbone of the DateWise TLS framework for downstream timeline summarization, we streamline the encoding of a document, enabling efficient handling of date and sentence selection within a compact representation space in date and sentence embeddings. The experimental results demonstrate that our MegraTLS prominently promotes existing TLS frameworks in timeline summarization performance. Besides, the MegraTLS exhibits robustness and insensitivity to parameter changes. Case studies of financial events illustrate that our model accurately captures key timeline events when applied to specific fields, providing more industry-specific valuable information than ground truth while maintaining simplicity. Although our experiments are conducted exclusively on Chinese datasets, the methodological insights and empirical findings of this work offer transferable, actionable, and robust guidance for deploying timeline summarization technology in domain-specific industrial applications.

Time series anomaly detection (TSAD) is a vital yet challenging task, particularly in scenarios where labeled anomalies are scarce and temporal dependencies are complex. Recent anomaly assumption (AA) approaches alleviate the lack of anomalies by injecting synthetic samples and training discriminative models. Despite promising results, these methods often suffer from two fundamental limitations: patchy generation, where scattered anomaly knowledge leads to overly simplistic or incoherent anomaly injection, and Anomaly Shift, where synthetic anomalies either resemble normal data too closely or diverge unrealistically from real anomalies, thereby distorting classification boundaries. In this paper, we propose CAPMix, a controllable anomaly augmentation framework that addresses both issues. First, we design a CutAddPaste mechanism to inject diverse and complex anomalies in a targeted manner, avoiding patchy generation. Second, we introduce a label revision strategy to adaptively refine anomaly labels, reducing the risk of anomaly shift. Finally, we employ dual-space mixup within a temporal convolutional network to enforce smoother and more robust decision boundaries. Extensive experiments on five benchmark datasets, including AIOps, UCR, SWaT, WADI, and ESA, demonstrate that CAPMix achieves significant improvements over state-of-the-art baselines, with enhanced robustness against contaminated training data. The code is available at https://github.com/alsike22/CAPMix.

The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assumptions face the following three limitations: (1) A single assumption could hardly characterize the whole normality or lead to some deviation. (2) Some assumptions may go against the principle of AD. (3) Their basic assumption is that the training data is uncontaminated (free of anomalies), which is unrealistic in practice, leading to a decline in robustness. This paper proposes a novel robust approach, RoCA, which is the first to address all of the above three challenges, as far as we are aware. It fuses the separated assumptions of one-class classification and contrastive learning in a single training process to characterize a more complete so-called normality. Additionally, it monitors the training data and computes a carefully designed anomaly score throughout the training process. This score helps identify latent anomalies, which are then used to define the classification boundary, inspired by the concept of outlier exposure. The performance on AIOps datasets improved by 6% compared to when contamination was not considered (COCA). On two large and high-dimensional multivariate datasets, the performance increased by 5% to 10%. RoCA achieves the highest average performance on both univariate and multivariate datasets. The source code is available at https://github.com/ruiking04/RoCA.