In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.

翻译：在当代数字化背景下，各领域连续生成大规模流式数据已成为常态。然而，这些数据中大部分缺乏标签，给识别异常等罕见事件带来了挑战。在非平稳环境中，由于概念漂移导致模型性能随时间下降，这一挑战尤为严峻。针对上述问题，本文提出了一种名为VAE4AS（用于异常序列的变分自编码器）的新方法。VAE4AS将增量学习与双重漂移检测机制相结合，同时采用统计检验和距离检验两种方式。异常检测由变分自编码器实现。为评估VAE4AS的有效性，本文基于异常率低于10%且存在周期性漂移的真实与合成数据集开展了全面实验研究。结果表明，所提方法在性能上超越了强基线模型及当前最优技术，为有效应对非平稳流式数据中异常序列检测的若干挑战提供了有力证据。