Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection

Xu Wang;Qisheng Xu;Kele Xu;Ting Yu;Bo Ding;Dawei Feng;Yong Dou
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Abstract

In the realm of Key Performance Indicator (KPI) anomaly detection, deep learning has emerged as a pivotal technology. Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. Despite recent progress in large models that show potential for handling complex, multidimensional tasks, the lack of extensive, high-quality datasets presents a significant barrier for directly training these models in KPI anomaly detection. This scarcity limits the models' ability to learn and generalize effectively within this specific domain. To overcome this, we propose an innovative approach to adapt fully pretrained large models from other domains to KPI anomaly detection, thereby mitigating data constraints and enhancing detection precision. Our approach involves adapting large models to anomaly detection tasks using patch operations and fine-tuning techniques, which significantly enhances the model's temporal dependency capture capabilities. Furthermore, to address the multivariate challenge, we introduce a novel feature extraction method based on channel independence to optimize information processing across multidimensional features. Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model's detection accuracy. By integrating these innovative techniques, we have developed a large-scale KPI anomaly detection model named ViTSD. Empirical evidence from experiments on five benchmark datasets and two additional datasets demonstrates ViTSD's superior performance, outperforming existing models across various evaluation metrics.
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关键绩效指标多元时间序列异常检测的大型预训练基础模型
在关键绩效指标(KPI)异常检测领域,深度学习已成为一项关键技术。然而,有效的深度学习模型的发展受到以下几个挑战的阻碍:稀缺和复杂的标记数据,数据处理的噪声干扰,在时间序列KPI数据中捕获时间依赖性的必要性,以及多变量数据分析的复杂性。尽管大型模型最近取得了进展,显示出处理复杂、多维任务的潜力,但缺乏广泛、高质量的数据集,这对在KPI异常检测中直接训练这些模型构成了重大障碍。这种稀缺性限制了模型在特定领域内学习和有效泛化的能力。为了克服这个问题,我们提出了一种创新的方法,将来自其他领域的完全预训练的大型模型用于KPI异常检测,从而减轻数据约束并提高检测精度。我们的方法包括使用补丁操作和微调技术使大型模型适应异常检测任务,这大大增强了模型的时间依赖性捕获能力。此外,为了解决多变量特征的挑战,我们引入了一种新的基于通道独立性的特征提取方法来优化跨多维特征的信息处理。此外,我们利用频域信息设计了一种特征增强方法,进一步提高了模型的检测精度。通过整合这些创新技术,我们开发了一个大规模KPI异常检测模型ViTSD。在5个基准数据集和2个附加数据集上进行的实验表明,ViTSD的性能优越,在各种评估指标上都优于现有模型。
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