Change Point Detection techniques aim to capture changes in trends and sequences in time-series data to describe the underlying behaviour of the system. Detecting changes and anomalies in the web services, the trend of applications usage can provide valuable insight towards the system, however, many existing approaches are done in a supervised manner, requiring well-labelled data. As the amount of data produced and captured by sensors are growing rapidly, it is getting harder and even impossible to annotate the data. Therefore, coming up with a self-supervised solution is a necessity these days. In this work, we propose TSCP2 a novel self-supervised technique for temporal change point detection, based on representation learning with Temporal Convolutional Network (TCN). To the best of our knowledge, our proposed method is the first method which employs Contrastive Learning for prediction with the aim change point detection. Through extensive evaluations, we demonstrate that our method outperforms multiple state-of-the-art change point detection and anomaly detection baselines, including those adopting either unsupervised or semi-supervised approach. TSCP2 is shown to improve both non-Deep learning- and Deep learning-based methods by 0.28 and 0.12 in terms of average F1-score across three datasets.
翻译:变化点探测技术旨在捕捉时间序列数据的趋势和序列的变化,以描述系统的基本行为。检测网络服务的变化和异常现象,应用使用的趋势可以对系统提供宝贵的洞察力,然而,许多现有方法都是以监督方式进行的,需要贴上标签的数据。随着传感器所产生和收集的数据数量迅速增长,我们越来越难甚至不可能对数据进行批注。因此,现在有必要提出一个自我监督的解决方案。在这项工作中,我们建议TSCP2采用一种新的自我监督技术,在与运动网络(TCN)进行代表学习的基础上,检测时间变化点。我们最了解的是,我们提议的方法是首先使用对比学习方法进行预测,同时进行目标变化点探测。我们通过广泛的评估,证明我们的方法超越了多个状态的改变点探测和异常点的基线,包括采用未经监督或半监督的检测基线。TSCP2在与TCN的学习中,用0.12和0.12这一方法改进了非持续平均学习方式的0.12和深层次数据。