无监督时间序列异常预测与基于重要性的生成对比学习
Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
AI总结：
本文提出基于重要性的生成对比学习（IGCL）方法，用于无监督时间序列异常预测，通过区分正常与异常前兆并自适应存储代表性异常前兆，提升预测性能。
摘要：
时间序列异常预测在许多现实场景中发挥着关键作用，例如环境预防和物理系统及时维护。然而，现有时间序列异常预测方法主要需要监督训练，并且需要大量手动标记数据，这在实践中很难获得。此外，在推理过程中可能会出现未见过的异常，这些异常可能与标记训练数据不同，使这些模型无法预测此类新异常。在本文中，我们研究了一个新的问题：无监督时间序列异常预测。我们提供了理论分析，并提出了基于重要性的生成对比学习（IGCL）来解决上述问题。IGCL区分正常和异常前兆，这些前兆由我们的异常前兆模式生成模块生成。为了解决潜在复杂异常前兆组合导致的效率问题，我们提出了一种具有重要性评分的内存银行，以自适应地存储代表性的异常前兆并生成更复杂的异常前兆。在七个基准数据集上的广泛实验表明，我们的方法在无监督时间序列异常预测问题上优于最先进的基线方法。
英文摘要：
Time series anomaly prediction plays an essential role in many real-world scenarios, such as environmental prevention and prompt maintenance of cyber-physical systems. However, existing time series anomaly prediction methods mainly require supervised training with plenty of manually labeled data, which are difficult to obtain in practice. Besides, unseen anomalies can occur during inference, which could differ from the labeled training data and make these models fail to predict such new anomalies. In this paper, we study a novel problem of unsupervised time series anomaly prediction. We provide a theoretical analysis and propose Importance-based Generative Contrastive Learning (IGCL) to address the aforementioned problems. IGCL distinguishes between normal and anomaly precursors, which are generated by our anomaly precursor pattern generation module. To address the efficiency issues caused by the potential complex anomaly precursor combinations, we propose a memory bank with importance-based scores to adaptively store representative anomaly precursors and generate more complicated anomaly precursors. Extensive experiments on seven benchmark datasets show our method outperforms state-of-the-art baselines on unsupervised time series anomaly prediction problems.
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。
