论文标题
时间序列更改点检测通过自我监督的对比预测编码
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
论文作者
论文摘要
更改点检测（CPD）方法确定与时间序列数据趋势和属性变化相关的时间，以描述系统的潜在行为。例如，检测与Web服务使用情况相关的更改和异常，应用程序使用或人类行为可以为下游建模任务提供宝贵的见解。我们提出了一种基于对抗性预测的编码（TS-CP^2）的自我监督时间序列更改点检测方法的新方法。 TS-CP^2是通过学习嵌入式表示形式来采用对比度学习策略的第一种方法，该表示的嵌入式表示将相邻时间间隔的嵌入成对与跨时间分开的间隔嵌入的对嵌入。通过对三个多样化的，广泛使用的时间序列数据集进行的广泛实验，我们证明我们的方法的表现优于五种最先进的CPD方法，其中包括无监督和半省和半求解方法。显示TS-CP^2可将使用手工统计或时间特征的方法提高79.4％，而基于深度学习的方法则相对于在三个数据集中平均的F1得分平均的方法提高了17.0％。
Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of the system. For instance, detecting the changes and anomalies associated with web service usage, application usage or human behaviour can provide valuable insights for downstream modelling tasks. We propose a novel approach for self-supervised Time Series Change Point detection method based onContrastivePredictive coding (TS-CP^2). TS-CP^2 is the first approach to employ a contrastive learning strategy for CPD by learning an embedded representation that separates pairs of embeddings of time adjacent intervals from pairs of interval embeddings separated across time. Through extensive experiments on three diverse, widely used time series datasets, we demonstrate that our method outperforms five state-of-the-art CPD methods, which include unsupervised and semi-supervisedapproaches. TS-CP^2 is shown to improve the performance of methods that use either handcrafted statistical or temporal features by 79.4% and deep learning-based methods by 17.0% with respect to the F1-score averaged across the three datasets.
